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DoE-Assisted Chromatography Method Development for the Analysis Biopharmaceuticals (2023-EU-30MP-1206)
Saturday, March 4, 2023
Cation-exchange chromatography (CEX) is the industry gold standard for the analysis of biopharmaceutical charge variants. However, the development of CEX methods in a time and resource-efficient manner constitutes a bottleneck in product characterization. CEX separations are complex and governed by multiple factors. Several scientific publications have proven the successful application of design-of-experiment (DoE) in chromatography method development. Nevertheless, performing DoEs with a large number of factors may be challenging, time-consuming, and expensive. This work illustrates the use of a split-DoE approach to aid the development of a CEX method for the analysis of the charge variants profile of a mAb candidate. Analytical method development was intended to provide a high-throughput (HT) CEX method to support charge variants analysis with minimal sample and time requirements. The split-DoE approach is based on fundamental knowledge of the CEX separation mechanism and aims to reduce the number of experimental runs whilst exploring a wide experimental space. Regression modeling was used to study the effect of both individual process parameters and their interactions on the separation efficiency to ultimately identify the optimal method conditions. This study provides an efficient workflow for leveraging the development of CEX methods. Hello, everyone. Thank you for joining my talk. I am Giulia Lambiase, I'm a Senior Scientist at AstraZeneca. I work in biopharmaceutical development in the analytical science team. Today, I want to talk to you about the use of DoE for the development of analytical characterization methods, most especially chromatography methods. In today's talk, I'm going to talk about therapeutic proteins, what they are, and why they are challenging for analytical testing, and introduce you to the use of design of experiment for analytical method development and the application of DoE for the development of charge variance method, specifically cation exchange chromatography method. To start off, protein therapeutics are inherently very complex due to their larger size and the presence of the post- translational modification, and also chemical modifications that can the protein undergo through during the processes of expression in cells, purification, and storage. Monoclonal antibodies dominate the biopharmaceutical market, representing about 70 % of the total sales of biopharmaceutical products. However, recently, there is a push for new products, next generation biopharmaceuticals, which are bispecific antibodies, antibody fragment, fusion proteins, and many other formats. All of them come with unique challenges due to their complex structure and presence of higher order structure, glyco forms, charge variants, the sulfate bonds, oxidized deamidated species, isomerization, aggregation, fragmentation. A ll of these modification, chemical process [inaudible 00:02:37] modification, can impact on potency, safety, quality of the final drug product. This is why thorough analytical characterization and analytical testing throughout all the stages of product life cycle is key to meet regulatory standards, to be enabled to deliver a product that meets regulatory quality profile. We use a plethora of analytical techniques for analyzing proteins, and these are based mostly on chromatography methods, electrophoretic methods, and [inaudible 00:03:25] . Due to the inherent structural complexity of proteins, analytical method development can be quite challenging. In today's talk, I'm going to specifically talk about chromatography methods and the use of design of experiment to help the development of chromatography separation. Chromatography method can be quite complex, especially if you have a complex analyte like a protein. This is because the separation depends on the interplay of several variables such as mobile phase composition, buffer pH, flow rate, column chemistry, temperature, the type of detector that you decide to use for the analysis. All of these parameters need to be fine- tuned and controlled during the separation process in order to achieve the desired separation. DoE can be very useful versus one factor at a time approach. One factor at a time approach involved the variation of one parameter at a time, maintaining the other constant. This may lead to a large experimental run, lack of information because there's lack of investigation on factors interactions. Lack of information also leads to additional experiments during method validation, which may lengthen even more the method development process and finally retard the overall product development. DoE, in comparison to one factor at a time approaches, DoE enables the variation of multiple parameters at a time. This allow, with a reduced number of experiments, to investigate a large number of factors, including the interactions between them. Also the development of mathematical models that allow the assessment of relevance and the statistical significance to facilitate all the steps required during method validation. DoE enables really to investigate a wide design space with less resources, so in a more efficient way. In fact, I like saying DoE enables faster, cheaper, and smarter experiments to deliver stronger and better analytical methods. In today's talk, I'm going to talk you through a split DoE approach for the development of a cation exchange chromatography method. Cation exchange chromatography is used for the analysis of charge variants. Specifically, if you see here on the left hand side of this slide, you can see a chromatogram of a protein where you can see some acidic species here on the left, [inaudible 00:07:17] on the left of a main species peak, and some basic species peak. All these acidic basic species can be formed due to the presence of chemical modification that can lead to superficial charge distribution variation in the protein. Cation exchange chromatography methods are quite complex chromatography methods because the separation efficiency is affected by a number of factors and quite sensitive to small changes of these factors such as column chemistry, mobile phase pH, temperature, flow rate, content of salt, time of the separation. In this approach, I'm going to talk you through an efficient way to develop cation exchange chromatography method using DoE. If you are familiar with DoE, you may know that often requires a sequential approach. In this experiment, I performed a main effects screening design for enabling the selection of the best column chemistry and the mobile phase pH for the charge variance separation of this specific mAb molecule. During the second DoE, I use response surface methodology, particularly a central composite design DoE, to optimize the chromatography separation by changing the flow rate and [inaudible 00:09:36] . Let's take into more detail in the first DoE experiment. This was a main effects screening design where I screened four column chemistry bought by four different providers, Agilent, Sepax, Phenomenex, and Waters, and I screened a range of pH from 5.5 to 6.5. My response was the experimental peak capacity, which is a parameter that tells you the efficiency of a chromatographic separation, precisely the number of peaks that can be separated within the chromatogram, the chromatography time that you set. Other parameters such as concentration of buffer, concentration of salt at the start of the chromatography gradient, flow rate, gradient time, shape, temperature, injection volume, concentration, and the UV absorbance were kept constant. These are the results for the first DoE. On the left hand side, you can see the four different column results. You can see how the experimental peak capacity changes versus the pH change in the mobile phase in all the four different columns. You can see that we aim to have high experimental peak capacity values. You can see that the Phenomenex column performed best. In all of these three columns, we can see that pH of 6.5 enables greater experimental peak capacities. But the Phenomenex column allowed for better separation results. It is also visible on the right hand side of this slide in the panel A. You can see at pH 6.5, how the separation differs when using different chromatography columns. We have Agilent , Waters , Phenomenex , and Sepax . Definitely, the separation of the charge variants using the Phenomenex column is much better than in the others because these acidic peaks are very well separated as well as these basic species here from the main product peak. Panel B, we have isolated only the results of the Phenomenex column. How the chromatography separation was that with the mobile phase or with pH 5.5, 6.0, and 6.5. We can see how the separation improves with the increase in pH. Obviously, the mobile phase pH is dictated by the intrinsic molecule pI. We could only investigate this range. Otherwise, the molecule would have struggled to find its own column. Based on our fundamental knowledge of chromatography separation with cation exchange columns, we decided that this parameter, so this Phenomenex column and pH of 6.5, were optimal to carry on development. We carried on with the second DoE using a central composite design. Central composite design is a type of DoE falling within the umbrella of response surface methodology, which is used for optimized conditions for investigating the presence of curvature, for instance, and extrapolate optimal values. In this case, we use our Phenomenex column and mobile phase pH of 6.5, and started to play with other parameters such as buffer concentration, concentration of the salt at the start of the gradient, and flow rate to investigate optimal conditions. Central composite design enabled to very efficiently, with a few number of runs, to identify optimal separation conditions, optimal method conditions. In fact, at the very end of the split DoE approach, we could say that with the investigation of four column, mobile phase, pH range, salt composition, gradient flow rate. With only 27 experimental runs, we could optimize a method for a monoclonal antibody. This method is very useful because it is now used as a quick, high throughput screening experiment. In a quick, high throughput analytical method for screening differences in the charge variance profile of these specific molecules expressed in different conditions and compare it to a standard. You can see here that the blue line is our reference standard and the red line is a stress material of the same molecule. You can see how the charge variance profile changed as a consequence of the stress condition applied to this molecule. This was achieved thanks to this analytical method which was developed and optimized with a DoE approach. We also decided to implement this DoE approach as a platform workflow for analytical method development for new products, new bio pharmaceuticals, and we screened a number of products. For all of them, we applied first the first main effect screen design, and we identified the best column and mobile phase pH to use. Secondly, we applied the central composite design to optimize the separation. Now, we have identified a platform column and a mobile phase composition for this class of therapeutics. When new molecules comes into the pipeline, we can very quickly, just by using a central composite design, which involves actually just 12 runs, optimize the chromatography profile and deliver an optimal cation exchange method for a specific product. The key take- home messages from my talk today is that DoE system method development followed by appropriate statistical analysis enables to plan experiment based on time, cost, and analytical resources available very efficiently, and schedule the execution of experiments with adequate sample type and size to extrapolate the maximum amount of information from our chemical data and efficiently address the challenges and goals of the intended research. It definitely saves time and cost for experiment execution in comparison to one factor at a time approaches. Most especially, it allows the complexity of analytical method development, but still interrogating several factor at a time and studying the effect of both individual method parameters and the interaction on the dependent variable. With today's talk, I hope I inspired you to apply more DoE in your experiments. Thank you very much, everyone, for your attention. If you have any questions, feel free to reach out to me. Thank you.
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Multivariate Analysis of Infrared Spectra Using Wavelet Model in FDE (2023-EU-30MP-1261)
Saturday, March 4, 2023
In catalyst development, tests to measure performances can be extremely time-consuming and expensive. In this study, we have explored the possibility of using faster and less expensive characterization data to validate mathematical models linking production parameters and performances. Specifically, we first modeled the production parameters to the performance data available in our dataset, using generalized regression, and from this model, we predicted a set of optimal production parameters. We then analyzed the Infrared Spectroscopy (IR) data using the Wavelet model in Functional Data Explorer plugging the production parameters as additional parameters. Thanks to this addition we were finally able to generate a synthetic spectrum for an optimal catalyst. The generated spectra combined with the predicted production parameters can be used by the scientist to more quickly understand the underlying mechanisms driving performances. Finally, a new catalyst material developed using the predicted parameters can be analyzed using IR and the synthetic spectra can be used to validate the model. Hello, I'm Chris Gotwalt with JMP, and my co- presenter, Giuseppe De Martino from Topsoe , and I are giving a presentation that tells the story of a data analysis project that showcases the new wavelet analysis in the Functional Data Explorer, one of the most exciting new capabilities in JMP 17. The case study begins with a product formulation problem where Tops oe wanted to design a catalyst that optimizes two responses, but the responses are in conflict with one another in that improving one response often comes at the expense of the other. A candidate for the optimal tradeoff was found with models fit by the generalized regression platform, and the optimal factor settings were found using the desirability functions in the profiler. This was a fairly standard DoE analysis. But in addition to the measured responses, NIR spectra were taken from some, but not all of the sample batches. This is information that can be used to give a clue to the R&D team about what the chemical structure of the ideal formulation should look like. In addition to the GenReg model of the responses, we also used wavelet models as the basis function of a functional DoE analysis of the spectra using the DoE factors as inputs. We were then able to get a synthetic spectra of the optimal formulation by plugging in the optimal factor settings found in the initial analysis of the two critical responses. Before going into the presentation, I want to point out that at the beginning of the project, Giuseppe was very new to JMP and didn't have a background in this type of statistical analysis. Giuseppe learned all he needed to do the analysis on his own after a couple of web meetings with me. This obviously shows he's a clever guy, but also that JMP makes learning how to do some very sophisticated data analysis projects quick and easy. Now I'm going to hand the show over to Giuseppe. Thank you, Chris. Here is some background about our project. It's a catalyst development project. Therefore, we are developing many different recipes. Each recipe has a unique set of production parameters, and once the sample is prepared, we characterize it in our analysis lab in Topsoe. And finally, we do a performance test. During the performance test, we look for two values that here we call Response 1 and Response 2. In this specific case, we are trying to minimize response 1 while maximizing response 2. That will lead us to this ideal space in the top left corner of the graph. But as you can see, the 55 samples that we've tested get stuck in the middle of the graph. That is because Response 1 and Response 2 are inter- correlated, meaning that improving one comes with the expenses of the other. Therefore, we move to JMP, and we try to look at our response data and our characterization data, try to see if we can move away from this line in the middle of the graph. We identified two targets areas that we want to reach, and together with Chris, we thought about using JMP to create a model that would connect the production parameters to the response values. T hen we further looked into our infrared spectroscopy data to try to validate our model and to get some extra information about the target samples. Here is an overview of the data set. We have produced 112 samples. Each sample has a unique set of production parameters. We have analyzed all the samples using infrared spectroscopy. We have one spectrum for each sample. Then we have used many other characterization techniques that we have in- house that accounts for 21 more columns of data. Finally, we have tested half of the samples and that accounts for the last two columns that we called response. At the beginning of our project, we actually wanted to include the infrared spectroscopic data in our larger data set, and that's why we wanted to use JMP, because now we have this new wavelet model possibility. And that would enable us to include the principal components coming from the wavelet model in our data set. And we could use that to create models in JMP. But before we start our analysis, we need to have a look at the Pro data to find outliers. We do that by clicking Analyze and the Multivariate Methods, Multivariate. H ere we can select our production parameters and characterization data as Y columns, and we get this scatterplot matrix. This is an example of just looking at the production parameters at what we would identify as an outlier. We can see that there is a set of points in production parameters too that is far away for all the other points. Furthermore, we have background knowledge about these samples that we know wouldn't be optimal for our catalyst development, so we decided to right click and say "Row, hide and exclude." We did this also with other points looking at the scatterplot metrics of all the characterization data. Now that we have cleaned up the data, we can fit a model. We click Analyze and Fit Model, and we select the production parameters as variables in our model. We click Macros and response surface to create a second polynomial combination of these variables. Then we select our responses as Y values. Then we decided to use generalized regression. H ere Chris can add some more info about why we decided to use this specific type of model. We used a quadratic response surface model because the design had three or more levels for each factor, so I knew that we would be able to fit curvature terms if necessary and also be able to fit quite a variety of different interaction terms. We use the generalized regression platform because it does model selection with non Gaussian distributions like the log normal. In my opinion, there aren't many reasons not to use the generalized regression platform if you have JMP Pro because it is so easy to use while in many ways being so much more powerful for DoE analysis than the other options in JMP and JMP Pro. After that, we can select our distribution. We know that the responses are going to be strictly positive, so we select the log normal distribution and then we click Run, and we say no. In this slide, we can see that we have now created a model, but we have also the possibility of creating other type of models using different estimation methods. We decided to use best subset. Here, Chris can add some more words about it. Well, so here we use best subset selection because the full model isn't terribly large, so why not try every possible subset of that full model and find the one that provides the absolute best tradeoff between accuracy and model simplicity? On the other hand, had there been eight or more factors, I would have used a faster algorithm like forward selection or pruned forward selection because with larger base models, it would take a very long time to fit every possible submodel to the data. We're going to be using the AICc model selection criteria to compare GenReg models. The AICc allows you to compare models with different effects in them as well as different response distributions. With the AICc, smaller values are better and the rule of thumb I use is that if a model has an AICc value that is within 4 of the smallest AICc value seen, then those two models are practically identical in quality of fit. If the two models have AICc values within 10 of each other, then they are statistically similar to one another. The main point here being that if we have two models and their AICc's differ by more than 10, then the data are pretty strongly suggesting that the one with the smaller AICc is the better model to be working with. As with any individual statistic, you should view the A ICc as a suggestion. If your subject matter experience strongly suggests one model over the other, you may want to trust your instincts and ignore the recommendation of the A ICc. Once we have created this new model, we can see that the non- zero parameters have now dropped from 16 to nine. If we want to compare, if the model has improved, we can look at the A ICc values. We can see that there is an improvement of more than 10, which is an important difference. Therefore, we decided to go with the best subset. We did the same for Response 2, and then we moved on. Now we can click on the red arrow and say profiler. In the profiler, we can play around with the production parameters and see how the model is expecting Response 1 and Response 2 to change. This is already a great tool for the scientist to understand how the model is expecting the responses to vary, but we can do more. We can click on optimization and desirability and desirability functions. Since from slide one, we know that we have two targets that we want to reach, we can change the desirability function to match those targets. So we double click on the Desirability function and we say match target and select the target area that we want to reach. Finally, we can click again on optimization and desirability and say maximize desirability. Here, the profiler will try to reach the optimal points for the production parameters. To summarize, we can say that now we have the first model, we go from production parameters to responses and we have set two targets that we want to reach. This way we can get ideal production parameters that we can communicate to the development team and they can use to move on in their research. In the second part of the presentation, I'm going to talk about how we use the IR Spectra. Here we have a file for each spectrum and therefore we need to click on file and import multiple files. Then we need to specify that we want the file name to be columned in the data set and then we can use this sample name, which is the name of the file as an ID to connect it to the other table where we have all the data. We click on the column and say link reference. Now that the two tables are connected, we can click Analyze and specialize modeling and functional data explorer. In the Functional Data Explorer, we want to use the intensity value as the Y output. We can use the sample name, matrix name as our ID function. Then this is very important. We use the production parameters as supplementary data. And the weight number is, of course, the X- axis. And we say, "Okay, here we can see that the data is already clean." We've imported all the Spectra and the data looks clean because I've done the preprocessing outside of JMP. I used Python because I'm more familiar with that and there is a very nice module that is able to remove the background and reduce the range that we want to look at. JMP was good to work with as an extra tool after this reprocessing. Then we decide to click on models and wavelets. We move from discrete data to continuous data. Now we can also look at the diagnostic plots. This is for you, Chris, to take talk about. It's a good idea to look at actual by predicted plots as you proceed through a functional data analysis. These have the actual observed values in the data on the Y axis and the predicted values on the X axis. We want the predicted values to be as close to the actual values as possible. A plot like this one that is tight along the 45 degree line indicates that we have a good model. Now, some of you may be concerned about overfit since the predictions fit the data so well. In my experience, I haven't found that to be a problem in the basis function fitting and functional principal component steps of functional DoE analysis. I'd also like to point out that in JMP 17, we've added a lot of new features for spectral preprocessing like standard normal variant, multiplicative scatter correction, and Savitzky-Golay filters . Those of you that don't know Python have access to these capabilities in JMP Pro 17. After that, we can also have a look at the Functional PCA analysis. Here, we'll spend some more words on it. After the wavelet model is fit, JMP Pro automatically does a functional principal components analysis of the wavelet model. This decomposes the spectra into an overall mean spectra and a linear combination of shape components and coefficients that are unique to each sample spectra. When we do the functional DoE analysis, GenReg automatically fits models to these coefficients behind the scenes and combines the resulting model with the mean function and the shape components to predict the shape of the spectra at new values of the input parameters. If we look at the principal component analysis, we can see that the wavelet model has created a mean function of all the spectra that we've set as input, and then it has created different shape functions. We decided to stop at six. What this shape function described is the variation of the data that we are analyzing. As you can see from the left, the first shape function is accounting for 72 % of the variation, while the second is accounting for 22 %. Together, the account called six account for 99.5 % of the variation. As an example, we can look at principal component 2, and we can see that around 3,737, there is a reduction. There is a minus. That means that increasing the principal component 2 will decrease the peak at 3,737. This is just an example to say that already from these six shape functions, we can get a lot of information. If we have subject matter knowledge, about the infrared spectroscopy and this catalyst system. Already here, we spent quite a lot of time looking at the principal components, but this is not what we are going to focus on in the next slides. What we want to look at instead is the functional DoE analysis. Here we have as well a profiler, but we can now plug the production parameters that we got from the first model that we developed. Therefore, knowing the target production parameters that we want to use, we can generate a fake spectrum or a synthetic spectrum, we can call it. This is a spectrum of a sample that was never produced. It could be wrong, but it can give some ideas to the scientists about what you would expect to get from these new production parameters. To sum up, we can say that now we move from production parameters to infrared spectrum. We have a second model that uses the wavelet model to generate synthetic spectrum. I imagine this to be like when you're baking a cake, now you have the recipe but you got also a snapshot picture of the final cake. It doesn't explain you how to do it but it adds information about what you want to achieve. Finally, we can move from model to test. That means that we can give the R&D team a new recipe and also the synthetic spectrum of that recipe. Together with this information, they can try to develop the new catalyst and see if the model is validated or is wrong. Another thing the group can do is look back at the previous samples. Now we have half of the samples that were not tested. Those are the black dots in the slide. We can look for outliers. Is there a sample that could perform really good that we haven't looked at? We actually have one. So that's another test we can do. Looking at future work, I added this slide that was at the beginning just to say that we focused on the production parameters and the IR spectra, but we haven't really looked at these 21 more columns of characterization data. In the future, we could spend some time trying to identify the most predictive parameters in these 21 columns and create maybe a new model from this characterization data to the response and use this model as a screening model to avoid testing samples that would not perform as good. That's the end of the presentation.
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Development of Predictive Single Event Latchup Model
Saturday, March 4, 2023
cipal Technology and Development Engineer, Microchip Technology Rousset To address the new space market focused on cost reduction, the use of COTS (circuit on the shelves) products is a good option. To be compliant with space reliability, COTS must be evaluated and modified to meet space agency specifications, especially on Single Event Latchup (SEL). This effect occurs by the strike of a heavy ion on the circuit. The transmitted energy to the matter (LET) triggers the parasitic thyristor and induces the latchup. The SEL sensitivity is characterized by the LET threshold and the holding voltage criteria. Until now, to evaluate LET threshold, TCAD simulation and experimental tests were performed, but TCAD is time consuming and irradiation sessions are very expensive. An analytical model LET threshold is a solution to obtain a quick estimation at lower cost and could help to harden the product to radiation. JMP is well adapted to assist in building and analyzing the results of a design of experiment (DOE). Its profiler is adapted to find best combination of the inputs to meet LET threshold criteria. Hello, I am Laurence Montagner. I'm working for Microchip Technology Rousset. I am in Aerospace & Defense Group. I'm going to show you how we use JMP to develop a predictive single event latchup model. This project, called SELEST, started four years ago to develop an internal SEL prediction tool. This work was funded by the CNES, the French space agency. Two posters were presented at RADECS conferences in 2019 and 2021. In the first poster, it was a new approach and the feasibility of using an analytical model. That's what we are talking about today and that's how I'm going to present. The second poster, it was with a more accurate model using a neural network approach. The context, to address the new space market that's been low cost, that means that our circuits are going to be launched in low Earth orbit. We are going to reuse COTS— circuits on the shelves —to make them radiation tolerant to meet space agency specifications. We need to analyze a lot of product to know if we can make them [inaudible 00:01:33] tolerant. W e need a model and a predictive tool to gain the time and money before any experimental tests. When they are sent in space, those circuits are under radiations. We have several source of radiations: the sun, cosmic rays, and Van Allen belts. The sun and cosmic rays emit electron, protons, and ions. As for Van Allen belts, the inner belts emit proton and the outer one emits electrons. Those particles, when they strike our circuits, causes damages. We have two family of damages. One, the TID— the total ionizing dose—w e don't talk about it today. We are going to focus on SEE, single event effect. We are going to focus more specifically on single event latchup. This single event latchup leads the component to the destruction. That's why we need absolutely to predict this phenomenon. The mechanism on the single event latchup is very similar to the electrical latchup, but it is not provoked by the same causes. In the single event latchup, this is when heavy ions is striking sensitive devices as inverters [inaudible 00:03:33] . In the worst case, it is striking in the middle of these devices, and it triggered a parasitic thyristor, composed here of an NPN and PNP bipolars. When the supply of the circuit is upper, that's the Vhold of this parasitic thyristor. At that time, the thyristor is still on, and we can lead to destruction. As you have understood, this parameter of Vhold is very important for us. I just talked about energy, the energy of the heavy ions. There is a physical quantity very important, and is criteria of a space agency. This is the LET, linear energy transfer. This is amount of energy lost in the matter by unit track lengths. For ESA, European Space Agency, circuit is said to be immune to latchup when this value is above 60 mega-electronvolts centimeters square per milligram. Our objective is to be able to predict the V hold and the LET threshold of a circuit, so we need to build a model. We use TCAD Sentaurus to run some simulation to build the model with a DOE. For the DOE, we need to define the input and outputs. As you have guessed output, V hold and LET threshold. Regarding input, for the first try, we decide to define four output, two from the process, epi thickness and epi do se, and two from the design, the length between the two well of the test structure of the inverter, and the length between the top and the well . Keep in mind that if the Vhold obtained by simulation is upper of the supply of the circuit, at that time, the circuit is immune to SEL. If the Vhold is inferior to the supply of the circuit at that time, it is possible to have a single event latchup, and we are very interested to know the LET threshold. In the flow, we are going to build a DOE with JMP, a Full Factorial DOE. We are going to input this DOE in the TCAD Sentaurus. We are going to run our simulations. We are going to take the output, Vhold and LET threshold. We put all results and input in JMP. We are going to screen data and build a model with JMP. Now, let's go to a JMP data table that we use to study the feasibility of using an analytical model. This is the table with our four input and two output here, and the full DOE, one value per color. To input here, we can see a preview of what we have in each distribution. To have a better display and have a better exploration of data, I begin by putting distribution of input and output. We can check that for each input, we have the same number at each value in each input, so it is okay. We can go at the end, and we can see that for the output, we have something to note quickly, that the V hold is inferior to the supply of the circuit, which is 1.95. I can put the limit on the graph. We can have a single event latchup. We have the value of the LET threshold in here. What we see that some value are upper that 100. We can highlight it to see if we see something special on our other input. We can note quickly that the Body Ties for one value, whatever other input value, are the source of the LET threshold upper of 100. If we can see upper value for the LET threshold upper of 60, there is not the same effect, maybe, for the EpiThick. With this first analysis, we can explore our data and have an idea of what we have. Second graph to be plotted for this analysis. It's a very interesting plot. Variability/A ttribute Gauge chart. We are going to plot all our input in X, and in Y, our output. Now, we can analyze first if we are main effect or interaction. We can connect cell means, and we can see that there is maybe a problem on the T CAD results. It is not a problem. We can continue our analysis and study to know if it is feasible or not to have the analytic model because it is just one dot, but we must note what happened here. We saw it quickly with this kind of graph. If there is a problem on our foot, if we have all our data for each condition. We have all data, but this one is to be analyzed. As for the Vhold, there is no problem on our output. We can see that what we know of the physics, that when the spacing between the two wells is higher, the Vhold is increasing. This result is consistent. If we see at the BodyT ie, the Body Tie is increasing, the Vhold is decreasing. That's what we know, too. Here we can see what we have already remarked on the previous analysis. Here, for this value of Body Tie, for the LET threshold, we are at a value of 100 mega- electronvolt. That's what we already noted. We have a trend that when the [inaudible 00:12:07] is increasing, the LET threshold is increasing, and it is in agree what we know. It is interesting. We can say that with this graph on Vhold , we have a main effect of the [inaudible 00:12:29] . We can keep the same graph, same analyzing, but by changing the order of the input. Re call , better. Remove this input, recall, and we can have the graph. What we can see on this graph, this representation, by just changing the order of the input, that the EpiD ose, for some value of other input have no effect for some condition here. So we can deduce there is interaction between inputs. For LET threshold , an interaction slightly here and no effect for other value. Now, we have checked, we have no problem on our data. We have seen we have some main factors. We can, by curiosity, use another tool. It is a p artition to know which input is the first to appear. Then on the LET threshold, what we see, so Body Tie first, that's what we have seen, and the EpiThick. We can continue by [inaudible 00:14:24] BodyT ie, too. Body Tie is very important. As for Vhold, the BodyT ie is important. A fter the spacing is… Now, we have a more accurate idea of what we have in our data, we can go and build our model. We put our input. I don't know what I have done. I remove our input. We have run a Full Factorial DOE . We try this model. We put our output, and we can run it. We can run the model. We can see that I missed something here. I already prepared something. Fit Model, EpiDose , Macros, Full Factorial. Okay, run. It's better now. Or is something wrong? I prefer this presentation with this plot, Actual by Predicted Plot. In this plot, we can see that the model is not really satisfying because we have a lot of dots far from the red area, even if the R-s quared is at 0.81, it's not fully satisfying. For the Vhold, we have the same remark. We can have a look at the Profiler. The Profiler can show us the good trend, but we are not going to have a long time on this. We are going to look at another model to know if we can have an accurate model than this one. We are going to try another one. Not so far. We are going to take our input, and we'll try Response Surface, and our output, and we run it. We are going to remove EpiDose because there is no effect. We can see that this model is better, the R-s quare is better. The V hold is a good model. Now, we have a look at the Profiler. The Profiler, the trends are good for all parameter. We note that there is maybe something to do because we see in this part of the curve, the LET threshold value are upper, and it is not in agree with the physics and what we know, of course. As I have already said, it is a first try. We need to work and work as we've done on the inputs and accuracy of the LET threshold to have better results here. You can see now in the EpiDose, we have quite no variation, a little variation here. However, this kind of graph is very interesting for us because when we want to make a radiation tolerant circuit, we can use it to help us by given a set of value. We can use here the Desirability Function. Now, we are going to set our desirabilities. Here, we want to be upper than 60 as a criteria of ESA. I put 60, 80, and 100, match target. Here, for the Vhold, we want to maximize it. I put this value, 2, 2.05. Now, I'm going to maximize desirability . Of course, it works. And n ow, not. Match target. Target . Now, maximize this desirability. Now, we have a set of value we wish because here, we have a range of value in the Body Ties here. We can play with it. Here, we can play here with the spacing. The other representation and the tool we like using in JMP is the Contour Profiler with some fixed parameter. We can have a contour plot here. If you want to change a design value, I put in axis the Body Tie and the Spacing _AC. We are going to put our famous value of 60 for the LET, the Lo L imit at 60. To have a LET upper than 60, we know we can have a value of Body Tie up to, if we take the cross here, about 6.5. We can use the range of the spacing. This tool, this representation, is very interesting for us to make our product radiation tolerant. This is not the last model we implement, but I can show you that in the red triangle, we can save the prediction formula in the table so that after, we can take it and encapsulate it if we want. Okay, that's all for me on JMP. Let's go back to the presentation. With this method, we built another model, this one using a neural network. The prediction obtained with this model was compared to a experiment on circuit. We can see that when the experiment shows there is no single event latchup, the prediction by the SELEST, the internal tool, said the same thing, there is no latchup. When experimentally there is a latchup, SELEST, even if there is a difference between experimental and prediction, show there is a latchup. For us, it was a good result. Not so accurate. That's why the work continue on this model to have an accurate model. That's why we continue working on DOE. We do it per technology node for having a better accuracy of this model. Okay, thank you for your attention.
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Approaches To Comparisons with JMP® 17 (2023-EU-30MP-1193)
Saturday, March 4, 2023
It is common to need to compare two populations with only a sample of each population. Statistical inference can help the comparison. Our presentation is about inference involving two hypotheses: the alternative hypothesis and the null hypothesis. Sometimes the goal is to provide sufficient evidence to decide that there is a significant difference between two populations. The goal at other times is to provide sufficient evidence that there is significant equivalence, non-inferiority, or superiority between two populations. These two situations require different tests. We will review these situations, appropriate hypotheses, and appropriate tests using common examples. Another common comparison is between two measurements of the same quantity. Our presentation will focus instead on the Method Comparison protocol for chemical and biological assays used by pharmaceutical and biotechnology development and manufacturing. We will present two methods that are available in JMP® 17 to assess the accuracy of a new test method against an established reference method. One method is known as Deming regression or Fit Orthogonal in JMP. The second method is known as Passing-Bablok regression. We will review the background of assessing accuracy and the unique nature of data from method comparisons, and demonstrate both regression methods with examples. Hello, and welcome to our presentation, Approaches to Comparations with JMP® 17. My name is Mark Bailey. I'm a senior Analytics Software Tester, and my co- presenter today is Jianfeng Ding, a Senior Research Statistician Developer. Before we get into the new features, we're going to take a moment to make sure that everyone has the proper background to appreciate these new methods. This has to do with using statistical inference when we're trying to compare two populations. This is a very common task, and the comparison usually leads to a decision between two ideas about these populations. If we could observe the populations in their entirety, we wouldn't need statistics, but that's not usually the case. So, we have to work with samples from the populations. Statistical inference can provide some really valuable information about those samples. In particular, is there sufficient evidence to reject one idea about the two populations? So a clear statement of these ideas or hypotheses is essential to making the correct choice for the test and also for the correct interpretation. So let's talk a little bit about the ideas or hypotheses that are part of these statistical tests. The alternative and null hypotheses, as they're known, represent mutually exclusive statements about these populations, and no other hypothesis is possible. For example, one statement might be for population A and population B that their means are equal. The other idea is that they're not equal. Those two ideas or hypotheses are mutually exclusive and no other hypothesis is possible. What's the role of these two ideas? The alternative hypothesis states the conclusion that we would like to claim. It represents the populations and it will require sufficient evidence in the data to overthrow the other hypothesis. The other one is called the null hypothesis. It states the opposing conclusion that must be overcome by strong evidence. It serves as a reference for this comparison. It's assumed to be true. Now, currently, there's somewhat of a misunderstanding in hypothesis testing in that it only thinks about comparison in one direction. That's because historically, this is the way it was presented in training. That most often taught test is used to demonstrate that there's a difference between two populations. The resulting lack of understanding can lead to a misuse of these tests. To be clear, the choice of the test is not a matter of what data is collected or how the data is collected. It's entirely about the stated hypotheses for the purpose of your comparison. Let's look at these two possibilities. Let's say that the goal of our comparison is to demonstrate a difference. We want to explicitly state these ideas to make sure the test is clear. In the first example, I want to demonstrate that a temperature change causes a new outcome. That is, there is a difference. We'd like to claim that the new level of the response will result from a change in the process temperature. Perhaps we expect a higher yield or we'd like to show more stability. A design experiment is used to randomly sample from the population for a low- temperature condition and from a population of a high- temperature condition. The two hypotheses for this test, the null hypothesis states that the temperature does not affect the outcome. Remember, it's our reference and we assume it to be true. The alternative hypothesis is our idea. Temperature affects the outcome, but we can decide this only if the evidence is strong enough to reject the null hypothesis. Now, let's change that. Let's reverse that. Let's say that in this comparison, I want to demonstrate equivalence. In the second example, I want to show that a temperature change does not cause a change in the outcome. That is, the outcome, either way, is equivalent. We want to claim that a planned change in the process temperature will improve the yield but not affect the level of an impurity of the product. We design the same experiment, collect the same samples, but now our hypotheses are switched. The null hypothesis is that the temperature affects the outcome. It's our new reference, we still assume it to be true, while the alternative hypothesis states that the temperature does not affect the outcome, the impurity level. But we can make that claim only if the evidence is strong enough to reject the null. So do I test for a difference or for equivalence? The key is how you state your hypotheses. These two examples, I think you can see, use identical data but different tests. The choice of the test is not about the data. It's about the claim that we want to make and how we state that properly in the hypothesis. Remember that statistical tests are unidirectional. That is, we can reject a null or not. If the test is to reject a null hypothesis with a high probability when it's false. Now, let's get to the new features in JMP® 17. Jianfeng will now present the equivalence tests, and when she's finished, I'll present method comparison. Now, I'm going to share my screen. Hello, my name is Jianfeng Ding. I'm a Research Statistic Developer at JMP® 17. In this video, I'm going to talk about the Equivalence, Noninferiority, and Superiority Test in JMP® 17. The basic hypothesis test on the left is a test that most quality professionals are familiar with. It is often used to compare two or more groups of data to determine whether they are statistically different. The parameter theta can be a mean response for continuous outcome and a proportion when the outcome variable is binary. Theta T represents response from treatment group and theta zero represents the response from a control group. There are three types of the basic hypothesis test. The first one is two-sided test and the rest are one- sided tests. If you look at the two-sided test on the left, the no hypothesis is that the treatment means are same and the alternative hypothesis is that the treatment means are different. Sometimes we really need to establish that things are substantially the same, and the machinery to do that is called an equivalence test. An equivalent test is to show the difference in theta T, theta zero is within a pre- specified margin delta and allow us to conclude equivalence with a specified confidence level. If you look at the equivalence test, the no hypothesis is that the treatment means are different and the alternative hypothesis is that the treatment means are within a fixed delta of one another. This is different from the two- sided hypothesis test on the left. Another alternative testing scenario is the noninferiority test which aims to demonstrate that results are not substantially worse. There is also a testing scenario called superiority testing that is similar to noninferiority testing, except that the goal is to demonstrate that results are substantially better. There are five different types of equivalence type tests. Depend on the situation. When should we use this test? Will be discussed next. These tests are very important in industry, especially in the biotech and pharmaceutical industry. Here are some examples. If the goal is to show that the new treatment does not differ significantly from the standard by more than some small margin, then equivalence test should be used. For example, a generic drug that is less expensive and cause few side effects than a popular name- brand drug. You would like to prove it has same efficacy as the name- branded one. The typical goal in noninferiority testing is to conclude that a new treatment process or product is not significantly worse than the standard one. For example, a new manufacturing process is faster. You would make sure it creates no more product defects than the standard process. A superiority test try to prove that the new treatment is substantially better than the standard one. For example, a new fertilizer has been developed with several improvements. The researchers want to show that the new fertilizer is better than the current fertilizer. How to set up the hypothesis? The graph on the left summarizes five different type of equivalence type tests very nicely. This graph is created by our SAS/STAT® colleagues, John Castelloe, and Donna Watts. You can find their white paper on this on the web. Choosing which test depend on the situation. For each of this situation, the region that we are trying to establish with the test is shown in blue. For equivalence analysis, you can construct an equivalence region with upper bound theta zero plus delta and lower bound theta zero minus delta. You can conduct an equivalence test by checking whether the confidence interval of theta lies in entirely in the blue equivalence region. Likewise, you can conduct a noninferiority test by checking whether the confidence interval of theta lies entirely above the lower bound if larger theta is better or below the upper bound if smaller theta is better. These tests are available in JMP® 17 in one way for comparing normal means and in contingency for comparing response rates. The graphical user interface of equivalence test launch dialogs makes it easy for you to find the type of test that correspond what you are trying to establish. A forest plot in the report summarize the comparison very nicely and makes it easy for you to interpret the results. Next, I'm going to do a demo about equivalent test, superiority test, and noninferiority tests. I'm going to use a data set called the Drug Measurement that is in the JMP sample data library. Twelve different subjects were given three different drugs A, B, and C, and continuous measurement were made. I first launched Fit Y by X and put the measurement as Y and drug type as X factor. This will bring the one- way analysis. Under the red triangle menu, let's first find equivalent test and there are the two options, means, and standard deviations. We're going to focus on means for this example. This will bring the equivalence launch dialog. In this section, you can choose which test you would like to conduct, and the graph represents the choice of the selected test. For superiority and noninferiority test, there are two scenarios. One is large difference is better. Another is small difference is better. Choose which one depend on the situation. You need to specify the delta or the margin for the test. You also need to specify the confidence level alpha for the test. You can either choose the Pooled Variance or Unequaled variance to run the test. For this example, we run the equivalent test first and we specify three as a difference. We are going to do the equivalent test for all the pairs. Click okay and it will bring the equivalent test result. On the top, it is the statistical detail for the equivalence test, and the bottom it's a forest plot. You notice there are two regions. The blue region are the equivalence region and the red are the non- equivalence region. The lines here represent the confidence interval of the mean difference between two groups. If we look at this line, this is the confidence interval of the mean difference between drug A and C, and you see this line is completely contained inside this blue region. We look at the P value of the equivalence test that is 0.02 which is smaller than 0.05. So at this 5 % significance level, we can declare that drug A and C are equivalent. But when you look at the confidence interval of a mean difference between drug A and B, and drug B and C, they are lined beyond this blue region. So we conclude at the 5 % significance level drug A and B, and drug B and C, we cannot conclude they are equivalent. Next, if we assume drug C is a standard drug and you would like to find out if drug A and B are better than drug C, that make us want to do a superiority test. Let me close this outline note for now and we launch the equivalent test again and we click Means. This time we're going to run superiority test. We click the superiority test and we prefer large difference and we specify 0.4 as our margin. This time we need to set drug C as our control group and click OK. This will bring the superiority test. From this forest plot you can see the confidence interval of a mean difference between drug B and C is completely contained inside this blue region. So we conclude that at the 5 % confidence level, we can declare that the B is superior to drug C. But we cannot make a same conclusion to drug A and C. This concludes my first example. The next example will show how to conduct a noninferiority test for the relative risk between two proportions. Let me open the data table. A randomized trial is to compare drug FIDAX as an alternative to drug VENCO for the treatment of colon infections. The two drugs have similar efficacy and safety. Two hundred twenty- one out of 225 patients treated with FIDAX achieved clinical Q by the end of the study, compared to 223 out of 257 patients treated with VENCO. We launch Fit Y by X again and we plug in Q as Y and drug as X factor and account as a frequency and click OK. Will bring the contingency analysis. From the Likelihood Ratio test, Pearson test, and Fisher's Exactly test, they all indicate that there is no big difference between these two drugs. But we would like to find out if drug FIDAX is not inferior to drug VENCO. We go to the up red triangle and bring the equivalent test. There are two options. One is the risk difference and one is relative risk. For this example, we choose relative risk. Again, bring this equivalent test launch dialog. For this example, we're going to run noninferiority test. A gain, we prefer large ratios. We specify 0.9 as our margin and we care about the treatment effect. So we should choose Yes and then we click OK. So this bring the noninferiority test. From the forest plot, we can see the confidence interval of the relative risk is completely contained in this blue region, and the P value for the noninferiority test is very small. So we conclude at the 5 % significant level, the drug FIDAX is not inferior to drug VENCO. This concludes my talk and I will give back to Mark. I need to stop this share. Thank you, Jianfeng. Now in the last part of our presentation, I'm going to talk about another comparison where we want to compare the results or measurements from two different methods of measuring some quantity. We assume that there's a standard method that already exists. It has been validated. We can use it to measure the level of some quantity. That might be the temperature or the potency of a drug. But for some reason, we've developed a new method for the same result. We must compare its performance to the standard method before we use it. This is a long standing issue. This comparison has been codified for a long time by numerous international organizations. I've listed a few of them on this slide. So this is a very well studied and an established comparison. In this case, we're going to compare to identity. We're going to compare these two methods where ideally the test method would give us the same value as the standard method. So we plot the data using a scatter plot. We have the test method on the vertical axis and the standard method result on the horizontal axis. We can even plot the identity line where Y equals X for reference. Ideally, we would get the result from both methods, but that won't happen because of measurement error in both methods. We'll use regression analysis to determine the best fit for this line where we have the test method versus the standard method. Then the estimated parameters for our model can be compared to the identity line. The null hypothesis, we start with the idea that they are not the same. So the intercept of this line is not zero or the slope is not one or possibly both. In other words, the results are not equivalent. The alternative, which we would like to claim is that they are equivalent. So there we state that the intercept should be zero and the slope should be one. To make this comparison using regression, we have to postulate a model. In this case, it's a simple linear regression model. We have a constant term A, a proportional term B times X. We're going to estimate those parameters A and B, and use our hypotheses to decide. We also have a term epsilon, and that represents the measurement error, the random variation. Using linear regression, we assume that the Y and the X are linearly related. We assume that the statistical errors that epsilon are in Y, not in X. We also assume that those errors are distributed in a way that are independent of the response. In other words, the statistical error is the same across the entire range of this method. Also, that no data exert any excessive influence on these estimates. Well, in method comparison, we usually violate these assumptions. First of all, there is measurement error in the standard method as well. Also, often the errors are not constant. That is, we observe a constant coefficient of variation but not a constant standard deviation. Outliers are present that can strongly influence the estimation. Other regression methods are required in such a case. The Deming regression simultaneously minimizes the least square error in both Y and X. That's appropriate for this case. The Passing- Bablock regression is a nonparametric method that's based on the median of all possible pairwise slopes. It's resistant to outliers and nonconstant errors. Let's talk about each of these briefly. The Deming regression is provided in the Bivariate platform through the Fit Orthogonal Command. This has been available in JMP for many years. The Deming regression can estimate the errors in Y and X, assume that the errors in Y and X are equal, or use a given ratio of Y to X error. Passing- Bablock regression is new. JMP® 17 introduced this method in the Bivariate platform through the Fit Passing-Bablock command. This command also includes checks for the assumptions that the measurements are highly positively correlated and exhibit a linear relationship. Method comparison often includes a comparison by difference. The Bland- Altman analysis compares the pairwise differences as Y to the pairwise means as X to assess bias between the two values. The results are presented in a scatterplot of Y versus X for your examination and also to identify any anomalies. This occurs in the Matched Pairs platform. The Matched Pairs platform has been part of JMP for many years as well, but the Bland-Altman test is a new addition. The report also presents the hypothesis test. Now I'd like to demonstrate these two methods. As I said, Deming regression has been available for a long time, but for completeness' sake, I'm going to demonstrate it here alongside the new methods. I select from the Analyze menu, Fit Y by X. I'm going to compare test Method 1 to my standard method. The standard method goes in the X role and the new test method goes in the Y role. You could evaluate more than one test method at the same time. Here's my plot. Initially, I see a scatter plot. I expect these two to agree very well. I expect to see that they follow this diagonal path, that they're linear, and so forth. I'll click the red triangle next to Bivariate and select Fit Orthogonal. In this case, I don't really know that the variances are equal or I don't have any prior information that I could specify a ratio. So I'll have JMP estimate the errors in both. I'll use the first option here. Now we have the fitted line using Deming regression. Below that we have the report. The report includes an estimate of the intercept. We can see it's small and close to zero. We have an estimate for the slope. Using the confidence interval, we see that it includes one, which we would expect if the test method agrees with the standard. That's Deming regression. Now, we're going to take a look at the new methods Passing-Bablock regression in Blad- Altman. Same start, select Analyze, and then select Fit Y by X. I'm going to use the Recall button here. I want to compare Method 1 to the standard, but I'm going to use a new regression technique. I'll click on the red triangle and select Fit Passing-Bablock. There's actually two lines here. There's a red line that represents the best- fit line using the Passing-Bablock regression. But there's also, for our reference, a line that represents where y equals x. It's hard to see. I'm going to use the magnifier tool to magnify a few times. Now you can see that there are, in fact, two separate lines. One is the identity and one is the fit, but they overlap quite a bit. These are quite similar. In the numerical reports, first I have a test for the high positive correlation. We're using Kendall's Tau, and we can see that it is highly significant. We reject the idea that they're not strongly correlated. Next, we have a test of linearity. Here, this test assumes that they're linear, and w e're looking for strong evidence against that. But we have a very high P value here, so we do not reject the assumption that they're linear. Finally, we have the parameter estimates by using Passing -Bablock regression, we have the point estimate, we have the interval estimate. For the intercept, that interval includes zero, so we cannot reject an intercept of zero. We have the slope is contained within an interval that includes one. Similarly, we can't reject that the slope of that line is equal to one. Let's say we'd also like to compare these two methods by difference. To do that, I click on the red triangle for the options of the Fit Passing-Bablock results, and here we see the command Bland-Altman analysis. It takes all the information here and launches match pairs with the additional information in the Bland-Altman analysis. The plot is showing us on the Y axis the pairwise difference between the method one and the standard method, and it's plotted against on the horizontal axis is the mean of those two values. The Bland-Altman analysis is helpful because it gives us an idea about the bias. So here I have an estimate of the bias is negative 0.113, but we can see that the interval estimate of the bias includes zero, so we can't reject the idea that the bias is equal to zero and so on. Now we have in JMP® 17, a much more complete set of tools for comparing different test methods. That concludes our presentation. Thank you.
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Yield and Quality Issue Solving by Correlating Optical Inspection Step Results With Electrical Tests (2023-EU-PO-1343)
Saturday, March 4, 2023
In the semiconductor manufacturing industry for automotive, parts are tested at each manufacturing step to screen likely-to-fail parts. The further upstream the weak parts are scrapped, the lower the scrapping cost will be. But testing has a cost as well. A recent project at NXP sought to avoid a manual defect classification of the defects observed at the wafer inspection level. Defects are now classified as killer or not-killer from a training image dataset, and a failure probability is assessed for each die. JMP® allows a further step in correlating this failure probability to electrical tests with three types of analysis. The first analysis assessed a failure probability threshold to limit the number of parts tested to limit test cost. The second analysis highlighted the tests most correlated with failure probability. The final analysis used the list of highlighted tests to adjust test limits to screen the parts with failure probability outliers. The analyses limit test costs while increasing quality. (view in My Videos)
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Applications of MSA Platform Tools in JMP® 17 (2023-EU-PO-1345)
Saturday, March 4, 2023
Measurement System Analysis is a methodical approach for identifying and managing the sources of variation that can influence the measurement system. At the top level, this type of analysis enables the quantification of the measurement system variation (MV) present in the Total Observed Variation (TV) of a process or system. It separates it from the Process (part-to-part) Variation (PV). The measurement variation can be broken down further into Precision and Accuracy. For the Precision component, we follow a sequential method for Continuous Data to determine the adequacy of a measurement system. A Type 1 Gauge Study examines the accuracy and consistency of the measurement device, and a Full Gauge R&R Study explores the Repeatability and Reproducibility of the entire measurement system. This poster will demonstrate the practical application of these tools in JMP 17, as well as making reference to the new MSA Design tool to support the initial phase of measurement system analysis (data collection plan). Attached are the JMP journal and JMP data tables (including reports) presented in the video. Hope you enjoy ! Welcome to this post of presentation in the applications of MSA platform tools in JMP 17. Before we go into it, I just would like to give a brief description about Measurement System Analysis, short for MSA. So when we look at the total observed variation in the process, we use measurement system analysis to try to identify and manage the sources of variation that can influence the measurement system being used. It's a combination of measurement devices, people, procedures, standards, etc. So we can decompose that total observed variation into two other components, the process component, or sometimes called part to part variation. But what we're really interested with measurement system analysis is in this measurement system variation component. Looking at the measurement error that is associated with this measurement system variation, that can be broken down into other components, precision and accuracy. For the purpose of this poster, I will concentrate on the tools that enable us to identify sources of variation within precision and specifically repeatability and a little bit about bias component under the accuracy component. What we tend to do when looking at measurement system analysis, there are several methods involved, normally and particularly for continuous data. We start by examining the accuracy and consistency of the measurement device alone using a technique called type 1 gage study. This is sometimes also named as analyzing the pure repeatability of the system. So we have one single part, one single device if the measurement system requires a manual intervention, we can have one operator. But the idea here is that we start evaluating the pure repeatability of the system before going into more complex analysis where other sources of variation may be part of the measurement system, which essentially is the second step around what is called Full Gage R&R, which examines both repeatability and reproducibility. Last but not least, we have continuous gage linearity and bias study, but it's not going to be covered specifically in this poster. So let's have a look of what this means in terms of JMP 17. In the new version of JMP, we do have a new MSA method, type 1 gage study, that essentially is going to help us identify that initial phase of the analysis with regards to the pure repeatability of the system. So I'll show you a quick example of that of an output report with the Type 1 Gage R&R. And what you can see here is that by default, the report shows a run chart. So this looks at 30 repeats of the same part using the same device or equipment in order, so this timeline really helps us identify any special situation, any special measurements that didn't work very well. There is a reference that we're on the nominal. If you're using a reference part, for example, we can definitely identify whether the average of those measurements are in line with the reference part. That mean value can be added to the graph if we want to. As you can see, it's going to be on top of it. But if I remove the reference line, then you can see average and the reference are very similar for this example. We also want to have a look at this Type 1 Gage R&R study and have a reference around 20 % of our tolerance. What in the type 1 gage study we're doing, we are limiting the analysis to only 20 % of the total tolerance in order to assess whether the pure repeatability is acceptable or not. But this specification, if you will, for the type 1 can be all consented in the settings of this tool. It provides some summary and capability statistics, so the normal reference location and spread references, particularly when it comes to six standard deviations, the number of measurements taken and the tolerance. T hen here are the two limits above reference on the graph for the 20 % of the tolerance, so plus or minus 10 % of that tolerance. If you use to the process capability indexes, what you will see now for capability of the gage, CG and CGK, they are exactly the same. The biggest difference here is that obviously we're looking at the capability of the gage and assessing this variation with regards to, in this case, the 20 % of the tolerance, but suddenly we can have a look at both the variation relative to those spec limits and also variation and location for CGK. So this gives us a summary of some metrics to evaluate the Type 1 Gage R&R study results. There are some percentage being calculated as well in terms of percentage of variation with regards to repeatability, but obviously, if you're using a reference part that you already know its nominal value, we can evaluate not only the pure repeatability of the system in this case, but also the bias. So the difference between the reference value and the nominal value. So that can be actually added as an additional test if we want to, so this is really looking at the hypothesis testing case in terms of if the bias is equal to zero so either the average and the reference value are the same or very close to each other, and as you can see, the reported P value there, in this case, statistically, there is no significant difference between average and reference value. Another useful visualization within this tool is the history realm, so we can have a look at the distribution of the values of those measurements taken, in this case, those 30 measurements for reference, that can be customized in your report as we go along. So very quickly, we just have a great tool to initialize our measurement system analysis process by now having what is called a Type 1 Gage R&R Study as part of the MSA platform in JMP. What we sometimes do is we also have a second step before we go into what is called a Full Gage R&R assessment, both repeatability and [inaudible 00:07:43] , which can be called a Type 2 Study. We have an example here of that, so the only difference here is the fact that in between the 30 measurements, we've removed the part from a potential holding fixture in between each measurement. As you would expect, by doing that intermediate step in between all the measurements, then we expect to have more variation, and this is what we can see here now. So not only we have more variation, where you can also see that the average value of the readings are also much lower than its target location. By turning on the bias test, we will be able to see that now the P value when compared to the Type 1 study where we didn't remove the part in between measurements, the part was fixed and just measured 30 times consecutively, we now have a low P value showing that there is significant difference between the reference value and the average. T his can be built into several increment and sources of variation even before we start adding multiple parts and operators or equipment into this analysis. But if we do, JMP already had a gage on our study tool in previous versions. This is just a quick example, what that means in terms of variability of the gage, a nd in this case, we use the gage on our method involved. So if you go to Analyze, Quality and Process, there's an updated version for the Measurement System Analysis. In the MSA method, we can see now we have the Type 1 Gage study that I've used for both Type 1 and Type 2. The only difference there is that on the report, the output report for the Type 2, I just edited the title and called it Type 2 just to differentiate between the two output reports. But what we're seeing here for the Full Gage R&R or the variability analysis, I've used the Gage R&R method, and this is where we can also decide the type of model used, normally used as cross, so we can see all the effects crossed with each other in the analysis, as well as some additional options are also available. But in this report, I'm customizing, in this case, I've added some specification limits. Here, essentially, we're not looking at the reproducibility of the system. It's just another increment we evaluate repeatability in this case. We are using not one, but 10 parts in this case and evaluating that variation for five repeats of each part. As you can see, in the Gage R&R report and table, the reproducibility component is zero because we don't have additional equipment or operators being evaluated in this analysis, so all the variation in this study is due to repeatability. So this is the traditional output that you would get from the Gage R&R method inside of JMP for reference. To finalize the new tools involved in JMP 17 for the MSA platform, what I would like to highlight as well is that as part of the planning phase for any Gage R&R study, it's important to understand what is the method utilized, of course, but also what will be a good method of data collection. A s part of JMP, we now have as part of DOE special purpose, a new tool called MSA Design, and this enables adding factors like parts and operators. If I quickly show adding three factors there, I can identify what is the MSA role involved in each one of them, for example. This is a great opportunity during the planning phase to start to come up with the design that will help you doing the data collection even before any analysis is done. For more information about how to utilize the MSA design feature, you can follow this link, which will take you to the JMP user community video where Hyde Miller, JMP systems engineer, has provided more information about this tool. Hope this was useful for you. Thank you very much.
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Exploring Group Differences and Other New Features in Structural Equation Models (2023-EU-30MP-1296)
Saturday, March 4, 2023
The structural equation models (SEM) platform continues to grow and evolve into a more complete and powerful platform. An important feature added to SEM in JMP® Pro 17 is multiple-group analysis (MGA). MGA allows users to test for differences in parameters across populations by enabling the specification of models that can have group-specific estimates or equality constraints on parameters across groups. In this presentation, we will demonstrate the use of MGA and other new features in SEM using real data examples. We start with a simple regression example and then turn to a longitudinal analysis example that showcases the flexibility of MGA. Lastly, we show how survey development can be expedited by a new feature that links Exploratory Factor Analysis to the SEM platform. Hi, everyone. I'm Laura Castro- Schilo. I'm a senior research statistician developer working with the Structural Equation Models platform. I'm really excited today to show you some of the new features that we have in JMP Pro 17. One of the big ones is going to allow us to explore group differences. We're going to talk about that a lot today. Our plan for today is, hopefully, we'll spend most of the time in a demo, really showing you those new features. But very briefly, before we get into that, I want to remind you what Structural Equation Modeling is, why you might want to use it, and then hopefully through the demo, I'll show you how to use it. Now, our presentation today is not very long. What I also want to do is show you share with you some additional resources from previous discovery presentations and developer tutorials where you'll learn a lot more detail on how to use SEM. Now, the overview of new features we're going to cover, we're going to first talk about multiple group analysis and then some improvements that we've done for longitudinal modeling within SEM and for survey development. After we go over those, we'll go straight into the demo and really show you how all of those are used. Structural Equation Modeling is a very general analysis framework for investigating the associations between variables. Now, this is a very broad definition, and that's purposeful, because Structural Equation Modeling is a very broad technique where a number of different models can be fit. Here I've listed a few of the models that you could fit within SEM, but this is not an exhaustive list. It really is a a very flexible framework. A natural question that might come to mind is, if I can do some of these analysis somewhere else, then why would I want to use a SEM? Sometimes you might not need to use it, right? That would be just fine. But there are some circumstances in which SEM would be particularly helpful. I've included here a list of what those circumstances might be. The first is sometimes you might be interested in understanding the mechanisms by which things happen. This is a circumstance where SEM can be very useful. Oftentimes when you want to understand mechanisms, that means that you have variables that are both predictors and outcomes. Yet, not many statistical techniques allow you to specify models where you can have a variable be both a predictor and an outcome. That's actually something that is very natural in SEM. If this is something that you're working on, SEM could be very helpful for you. You really want to leverage your domain expertise if you're using SEM. The reason is because in order to specify your models, you really need to think about what are your theories? What is it that you know about your data? You come up with those theories, you translate them into a testable model, and then when you fit your models, you see whether or not there's support for those ideas that you have. Now, a very important use case for SEM is when you're working with variables that cannot be measured directly. So late- in variables are very important in a number of different domains. If you're interested, for example, in looking at customer satisfaction or quality of a product in the social sciences. There's so many late- in variables, personality, intelligence, those are some of the cliche things that you would hear. But really, late-in variables are all over the place. If you work with those, if you have research questions that entail late- in variables, then you're really going to benefit by using SEM. A somewhat related reason to use SEM is that if you have variables that have measurement error and you actually want to account for that measurement error, SEM can also be very helpful. I say that this is related to the late-in variables because the way in which we account for the measurement error is by specifying late-in variables in SEM. Measurement error can have sometimes unexpected consequences on our inferences. It can be quite useful to account for it. Another benefit of SEM, and this is one thing that is very practical really, is when it comes to having missing data, which, of course, are all over the place, the most popular estimation algorithm for SEM handles missing data in a seamless fashion such that the user doesn't really need to do anything. Missing data are handled with a cutting edge algorithm and you really don't have to worry about it as much. If you have missing data, sometimes I tell people even if you just have a simple linear regression, you can benefit from using SEM just because missing data are handled and it's easy. Lastly, Path Diagrams are a critical tool for Structural Equation Models. Those diagrams are very helpful because sometimes even the most complex statistical models can be conveyed in a very intuitive fashion by relying on these diagrams. In JMP, we use these diagrams to facilitate the specification of our models, but also to convey the results of models. Those diagrams can be very helpful when you're presenting your results to any type of audience, really. All right, so this is just a very brief list of why you might want to use SEM. I do want to share a link here to a presentation that I gave along with James Cuffler, who's also in job development. We did a developer tutorial where we actually went into a much more depth about the reasons why you might want to use SEM. If you want to check that out, I just wanted to share this link here. If you download the slides from the community, you don't have to type this long link. You can just click on that and check that video out. All right, so how to use SEM. Again, I'm going to go into a demo and I'll show you how to use SEM, but my demo is not going to be too focused on a tutorial type of presentation, mostly because of time constraints. We want to keep this short and sweet. What I want to do in this slide is share with you additional video presentations where you can go and learn more in a tutorial form how to use Structural Equation Models for a few different case studies, basically. Here, this first video is a link where I covered how to model survey data and latency variables. We cover things like confirmatory factor analysisand path analysis with and without latency variables. If you have longitudinal data, this video can be quite helpful. Here I went over how to fit late-in growth curve models and how to interpret those results. We'll do a little bit of longitudinal modeling here today in the demo, but we won't be able to be able to go into the details again in a tutorial way. I definitely encourage you to watch that if you are interested. If you don't have prior SEM experience, I very much encourage you to watch this other video where James Cuffler and I talked about building Structural Equation Models in JMP Pro. This one is very introductory, and so you might want to start with that one prior to going to the others. Okay, so now it's time to get into a little overview of the new features in JMP Pro 17. Multiple group analysis is a feature that I've been really looking forward to presenting on because this is something that extends all of the models that can be fit within SE M. It does so by allowing us to investigate similarities and differences across sub-populations. We do this by incorporating a grouping variable into our analysis. Now, the most popular multiple group analysis examples usually show a grouping variable that has few levels. Things like demographic variables are used very often. Indeed, in the demo I'm going to do, I'm also going to use a simple demographic variable. But really, there's no limit to how many levels you really should have. What really matters is how many observations do you have for each level. You want to have a relatively good sample size for each of those subgroups. Now, there is a general strategy for the analysis. We're going to see this in practice, but I want you to start thinking about how this really works. It's actually quite simple. What we do in multiple group analysis is fit two models. One of those models is going to be a more restricted version of the other. Once we fit both of them, we'll be able to do a likelihood ratio test or a high square difference test in order to make an inference about whether the restrictions that we imposed in one of the models are in fact tenable. This is how we figure out whether there are statistically significant differences across groups. Again, we'll see that play out in the demo . In terms of longitudinal data analysis, we've made it a lot easier to interpret the results from your models by looking at the model implied trajectories through a new predicted values plot. We also have made it a lot easier to specify multivariate growth curves. If you're familiar with these models, they allow you to investigate the association of multiple processes over time. They can be very helpful, but it used to be a little tedious in terms of how to specify those. Now we've done that very easy and fast through the use of model shortcuts. For some advanced applications, we've also made it easier to define an independence model based on what users want to have as the independence model. There are also some improvements for surveys. This really is mostly focused on streamlining your workflow for developing surveys. Usually the analytic workflow starts by using exploratory factor analysis, and then you take those results and confirm them with an independent sample using confirmatory factor analysis in SEM . What we've done with the help of Jianfeng Ding, who is the developer for Exploratory Factor Analysis, we've been able now to link the two platforms by basically allowing you to copy the model specification from exploratory factor analysis and then paste that into SEM so that you can easily and quickly confirm your results. We also have a new shortcut for switching the scale of your late-in variables. Sometimes this is helpful when you're developing surveys for specifying models. We also have a number of more new heat maps that are just going to make it easier to interpret the results of your analysis. Now, last but not least, our platform has always been very fast. But in this release, Chris Gotwell put a lot of awesome effort toward improving even more the performance of our internal algorithms. If you have lots of variables or lots of data, definitely give it a shot. I am very, very impressed and excited about what we have to offer in terms of the performance of the platform as well. Okay, so it's time for the demo. Let's go ahead and show you what I have over here. I have a journal where I'm going to work through, hopefully we have enough time to work through three examples. The first one here, perhaps not surprisingly, uses our big class data table. It's going to be a very simple example just to introduce the notions behind multiple group analysis. Now, what I'm going to do here, we have two variables, right? Height and weight. What I want to do is investigate the association between these two variables by sex. I'm going to go to the Analyze menu, go down to Multivariate Methods, and then Structural Equations Models. I'm going to use both of those variables and click on Model Variables. Now, the brand new feature of multiple group analysis can be found in this launch dialog under this Groups button. This button is new and that's what's going to allow us to select our grouping variable and click on groups in order to use that as our grouping variable. We're going to look at how males and females and whether they differ basically on their association between height and weight. We're going to click O kay. Now, this is the platform. You can see if you have seen our platform before, it looks very similar as before, with the exception of these new tabs right here. The tabs are there to tell us about the different groups that we have in our analysis. In this case, there's only two levels for our grouping variables. We have a tab for the females and a tab for males. One of the things you'll notice is that the Path Diagrams have a model already as a default for each of those groups. Those default models are the same. That's why when I switch tabs, nothing really changes. The Union tab, as the name implies, it shows us what's in common across all of our grouping, well, the levels of our grouping variable. Here, this is why this diagram also looks the same. In order to specify a simple linear regression in SEM , here, I'm just going to select in this From List the height variable, and then in the to list, I'm going to select weight. I'm going to link those two variables with a one headed arrow, which is what adds that regression path to my model. This is just a simple linear regression where height is predicting weight. Now, sometimes I like to right-click on the canvas of the Path Diagram and I go to customize diagram just to make the nodes a little bit larger because I find that sometimes, especially when the diagrams are small, that looks a lot nicer. This is just a simple linear regression. Now, notice that because I did my model specification under the Union tab, both the females and the males inherited those same changes and specifications to the model that I made. If I make any changes within a group specific tab, then those changes will only apply to that group. But in this case, what I want to do is fit an initial model where both males and famales and females get their own estimates for this linear regression. Now, keep in mind that the estimation of this model is all done simultaneously. We're not separately fitting this model for females and for males. Everything is done simultaneously, but I'm still able to allow each of the groups to have their own estimates for the model. I'm going to click on Run, and we'll see there's a model comparison table where we can learn a lot about the fit of the model. But now, something that's new in our report is that we have these tabs for each of our groups. We have a tab for the females and a tab for the males. Now, if you focus on the regression coefficient, for example, I can go back and forth and realize that I do have, in fact, a different estimate for that coefficient. Now, the coefficient looks different, but I don't have a test, a formal statistical test that tells me whether or not that association is statistically significant. The difference and the association is different. At any rate, the males here have about 3.4, female have a little bit larger value. But what we really want is to fit a second model where we force an equality constraint on that parameter estimate, and then we can use that to compare against this model. Let's go ahead and do that. I'm going to be on the Union tab and I'm going to select that regression path and I'm going to click the button, Set Equal. This is going to bring up this dialog which is just going to ask me to confirm that I do want to apply this equality constraint across all of my groups, which I do. I'm going to click Okay. Now notice that I have this new label that was put here on the edge. If I look at the female tab and the male tab, that label is still showing up on that edge on that arrow. That is our way to convey to you, the user, that the same parameter estimate is going to be used basically to describe that association. Okay, so let's go back and model name. We're going to change this to be regression effect is equal. We force that to be equal in this model. We're going to go ahead and click on Run. Now, again, we could look at our model comparison to look at the fit of my different models. I can select the two models that I just fit, and because one of those models is a restricted version of the other, we call this that the models are nested, we can actually do a likelihood ratio test. That is done very easily in our platform simply by selecting the two models and clicking on Compare Selected Models. We will obtain a difference in the Chi square, which represents the change in the misfit of the model. We also look at the difference in degrees of freedom between the two models and the differences in the Fit according to some of the most popular Fit statistics in SEM. Now, according to this specific test, it appears that the change in Chi square, the increase in misfit is not statistically significant. If we use just this Chi square difference test, we would then come to the conclusion that even though those two values are different, they're not statistically different. Now we could go back down here to our tab results and you can see that the regression coefficient is the same even when I go across the tabs. We could then say, well, there's no difference between males and females in terms of how height predicts weight. This is a very simple example of how we could use equality constraints across groups in order to test a specific hypothesis. Now, as you can imagine, I could go back into my model specification and I could put equality constraints also on the variance of height and on the residual variance of weight. If that is something that is of interest to me, if I want to test those differences, this framework allows me to do that. Now, a lot of times you're going to have more complicated models well beyond linear regression, or you might have more levels of your grouping variable, and that's totally fine. This is a simple example that hopefully you can... That allows you to see how you could extend this into a more complicated setting. Okay, so that is this example. I want to move on to an example that uses longitudinal data. Now, we're not going to move away from multiple group analysis entirely. We're basically going to highlight some of those longitudinal analysis improvements, but then still bring back the notion of multiple group analysis. For this example, I want you to imagine that we have data table where we have data from students that have taken an academic achievement test for four consecutive years. Perhaps what we really want to find out from these data is whether student's achievement... How is it developing over time? Whether males and females differ in their trajectories over time? These are going to be the two questions that we're going to focus on for this particular example. Now, there is a sample data table that you will find within our sample data folder. It's called Academic Achievement. You could use that to follow along with this example. In this data, we have 100 rows. Each row represents a different student that took this academic achievement test. You can see that here, these four columns represent the scores on that multiple choice test that was taken for years in a row. Those are the data that I'm going to focus on for fitting a longitudinal model. I'm going to go to the Analyze menu, Multi Variate Methods, Structural Equation Models, and those four variables are selected. I'm just going to click on Model Variables in order to use those in SEM. I'm going to click Okay. Remember, the first question was, how do students' academic achievement develop over time? We want to characterize that growth or figure out whether there is growth indeed. We have our model shortcut down at the bottom left, and you can see that under the Longitude analysis menu, we have a new option. We're going to get to this option later today, multivariate, late in growth curves. But we also have had a few other options here that make longitudinal modeling very quick and simple. For this example, I'm going to use the Fit and Compare Growth models. When I do that, three different models are fit. I obtain a Chi square difference test for all of the possible combinations here. If I look at the Fit indices and also the results from this Chi square difference test, I will recognize that the best fitting model here is the linear growth curve model. In other words, it appears that the scores on this academic achievement test over time can be best characterized by linear growth. Based on that, I will go ahead and focus on interpreting the results from this linear growth curve model. I'm going to open that and recall that that one of the new features for longitudinal modeling is a new predicted values plot that allows us to interpret the results of our models a lot more easily. If you're familiar with growth curve models, you know that some of the key parameter estimates are these right here. They tell us on average how our students where do they start and how are they changing over time and how much variability there is in those trajectories. Under the red triangle menu of this particular model, if I scroll down, I'm going to find an option called predicted values plot. If I click on that, you will see that as a default, we show you box plots of the predicted values for all of the outcome variables in the model. Now, when you have longitudinal data, we have a very convenient option here that allows you to connect the data points and actually obtain a spaghetti plot that shows you each of the individual predicted trajectories by the model. Now, it's pretty cool because the plot is, in fact, linked to the data table. Whatever selections you have here on the plot, you can also see those in your data table, which is something that you know to expect from JMP. In terms of interpreting the results of the model, it's no surprise that these are all straight lines because we fit a linear model. But you can certainly see that there is a lot of variability in the way these students are changing. Some students start on the top at the beginning and are still increasing. Other students are starting low and are actually exhibiting a little bit of decline over time. But we also see an average trajectory here that seems to show a little bit of increase over time. On average, there is some increase, but there's a lot of variability on how people are changing. Of course, one of the natural questions you might have is, what factors predict those different trajectories, like the variability in that intercept slope, that's something that I've covered in other presentations, so I'm not going to talk about that now. But again, I encourage you to use the predictive values plot to better interpret your longitudinal analysis. We talked about users being able to specify their own independence model. That is something that we do here in the model comparison table and can be very useful for longitudinal analysis. We do have an independence model that is fed by default, but if you choose to change that, then you could always right-click on any given model that you want to set as the independence independence model, and we will take care of that change for you. That is an advanced technique. I very much advise you to, if you're not familiar as to what's the proper independence model for your analysis, you should really take a look at the literature to make sure that you're using a good independence model because it really varies sometimes by context. I'm sitting next to this beautiful window and the sun, it's a gorgeous day so I'm going to have to adjust here my computer so that I don't have all the light on my face. I apologize for that. Okay, so let's get back to this question. We said that how do students achieve and develop over this period of time? We now have an understanding that it develops in this linear fashion and that there is substantial variability. That's the answer to that question. Well, the next thing is, do males and females differ in these trajectories? The way we're going to acknowledge and address that question is by using multiple group analysis. T his could be back in the platform, we could use the main triangle menu to redo and relaunch our analysis. Now what we're going to do is bring this grouping variable, I have a sex as a groups variable. Just by doing this, we'll be able to invoke our multiple group analysis functionality. I'm going to click Okay, and now you can see that our report for the platform has the levels of our grouping variable here as tabs. Just as before, you can see that the males and the females have the same model as a default, but we can make changes to that. We're going to work within the Union tab because I want the changes that I'm about to do to the model specification, I want them to apply for both males and females. I will also highlight, and this is just a little side note, that under the Status tab, you're going to find group specific information that we didn't have before when we didn't have multiple group analysis. You can have some information about your data, missing data, and so on that is specific to the groups. Okay, so let's go ahead and answer this question. Do males and females have differences in their trajectories? Well, I already know that the linear model fits best, so I'm going to go to our model Shortcuts, Longitude Analysis, and I'm going to click on the linear latency growth curve. The Shortcuts very quickly set up the model for me, make it very simple, and they do that across all of the levels of the grouping variable. I have the linear growth curve model. Notice that these key aspects of the model, the estimates that really characterize the change in our data, don't have any labels on those edges, which means that they're freely estimated across males and females. My first model here is a linear growth curve model. I'm just going to put a little keyword here. Oops, I erased it. Linear growth curve. But I wanted to include here that this is freely estimated right across the groups. I'm going to click on run. Excellent. We can see here some Fit indices. In my report, I can see a tab for the males and for the females. Of course, as you'd expect, if I go back and forth, I could take a look at the results for the females and then go back and look at how those results are perhaps different for the males. This is interesting. There appear to be some differences, but again, we might want to figure out whether the differences that we observe just from looking at these estimates but those are in fact, statistically significant. What I'll do is I'm going to go back to my model specification and I'm going to do an OmniVis test. In other words, rather than just putting an equality constraint on one of these estimates, I'm actually going to do that for all of these estimates, the intercept mean, the mean for the slope, and the covariance of the intercept and slope, and their variances. You don't have to do it this way, but really it's your research question that should be guiding where do you place those equality constraints? In my case, I just want to do an omniVis test where I figure out whether the trajectories for males and females are different and whether or not I need a separate estimate for those parameters. I have all of those edges selected and I'm going to click on set equal. Here I confirmed that I do want those equality constraints across both groups. This is actually quite helpful when you have more than two levels in your grouping variable. It might be that you want equality constraints across, say, two groups but not the third. You can uncheck some of those groups here if you needed to. I'm going to click Okay. Now all of those edges got a different label. You can see that if I go and look at the males and the model for the females, those labels are the same. Again, just to remind us that we're going to estimate only one estimate for each of those edges across groups. Okay, so this, once again, is a linear growth curve, but I have equal growth estimates. Let's go ahead and run that model. We can see, again, we could focus on the fit of this model. It doesn't seem to be as good as the previous one. Because this second model is a restricted version of the first, we can actually select those two models and do a meaningful comparison by clicking on Compare Selected Models. As before, we are able to see here the change in the Chi square along with the change in the degrees of freedom. This tells us how much increase in misfit is there in our model, and is that increase in misfit statistically significant? If it is, which in this case it is, then we basically are saying that those equality constraints are not tenable. It was not a good idea to place those. Now we can say with a formal statistical test that there are statistically significant differences in the trajectories across males and females. Now, you might want to look at those differences by using the predicted values plot. That's something that we can do just by going into the red triangle menu. But what I'm going to do first, actually is I don't really want to look at the model that has the equal growth estimates because we just realized that those equality constraints were really not a good idea. I'm not going to look at that. Instead, I'm going to look at the first model we fit, and I'm going to do the same for the males here. Under the red triangle menu, I'm going to click on Predicted Values Plot, and I'm going to connect those points because I know my data are longitudinal. This is the plot that is specific to the males, to the male sample. But it would be really helpful to look at this plot side by side with the plot for the females. It's actually quite nice that all of our red triangle menu options here are automatically turned on across all of your groups, so you don't have to go group by group turning on the things you want to see. But another trick that I really like is, when you have a tabbed report, you can always right- click on it and change the style of the report so that it's on a horizontal spread. This is going to allow you to see the tabs side by side, the content of them. I'm going to click on H orizontal Spread. Now notice that I have the males and the females side by side. I'm going to use the red triangle menu along with the option or Alt key in order order to turn off the summary of fit, the parameter estimates, and the diagram. Really, all I want to see is the predicted values plot. I'm going to click Okay. Perfect. Now I can see the predicted values plot for the males and for the females. I can see that side by side. Very purposefully, we have here the Y axis in the same scale so that these plots are comparable. Now you can see how the trajectories differ. We see that there's a lot more spread for the sample for the females. There also seems to be a little bit of a difference on that average trajectory in the amount of growth. Again, there's many more follow up tests that we could do here in order to figure out where the specific differences lie. If we wanted to test that, we could say, well, is there a difference specifically in the variance of the slope? We could put that equality constraint and do more specific tests as followups. But for now, I hope that just showing you this example really allows you to see how multiple group analysis can be used in a more complex setting and how this new predictive values plot can be used to really facilitate the interpretation of your longitudinal models. All right. We're almost at the end of the demo and what I want to do very quickly with the same data, I really wanted to highlight the multivariate growth curves shortcut. L et me go ahead and go back here to the Structural Equation Models platform, and this time, imagine that we have two sets of scores over time. So we're going to be looking at two processes. We don't just have academic achievement on that one test. We have it on two different tests and we want to see how those two processes are changing. How are they related over time? I'm going to use all of these variables here, click on Model Variables and Okay. U nder the model shortcut, remember that longitudinal analysis multivariate latent growth curve, that shortcut allows me to select variables for one specific process. Here, I might have those first four variables. That was my first process I want to look at. Let's just say that those were math scores. I'm going to call that math. Y ou get to choose here what type of growth you want to specify for that specific process for that set of variables. We're going to stick to a linear growth, and then we can click the plus button in order to have that done for us right away. Y ou can see the preview in the background here. W e have an intercept and a slope for math. T hen we can change the name here. Maybe the second process is science, and now we can select the variables, the repeated measures for that science test over the four years. A gain, we're going to stick to the linear model, and we're going to click the plus button. V ery quickly, that model is changing there on the background. We're done now. So I'm just going to click Okay. A gain, now I could just click Run and very quickly get the results for that model. This is an advanced application, but it is a really interesting one because it allows you to look at how the initial time point, the intercepts across two processes in this case, how are those intercept scores related? Are they associated? A lso the rates of change over time. If you have a higher score in math, do you also tend to have not just a higher score, but a higher slope over time in math? Does that mean you also have a higher rate of change in science? A ccording to this, you do, because we have a positive association between those two factors. Again, just highlighting some of that new functionality. My very last example is for survey development. This is going to be very brief, I promise. Let's just say here that we want to figure out what are the key drivers of customer satisfaction? W e know that perceived quality of our product and the reputation of our brand are really important. But really, before we can even answer any questions about customer satisfaction, we really need to make sure that we have a valid and reliable way to assess those variables. Because these are variables that are not observed directly, they're latent variables, and therefore, it's difficult to make sure that we are measuring them in a reliable and valid way. S urvey development is all about achieving that goal. I have an example here that is going to allow us to see how exploratory factor analysis is now linked to SEM so that you can do survey development in a really streamlined fashion. I have 843 rows in this data table. Each row represents an individual who filled out a survey. In that survey, they gave us ratings, answered questions about the perceived quality of our product. They also gave us different answers for the perceived brand of our brand. T hen they also answer questions about their satisfaction with the product. This could be things like, how likely are you to recommend our product to someone you know? Those types of questions. I already have a saved script for the factor analysis platform. I'm not going to get into the details of how you use this platform, but I do want to focus on the fact that the results from this analysis are right here in the rotated factor loading matrix. That is the key result from this analysis. U sually, what we want to see here is that the questions that are supposed to measure, in this case, satisfaction, that they are in fact, loading into the same factor. In this case they are, and that's good news. We see the same pattern for quality. The more substantial loadings are for these first three questions of quality. Notice that there is one quality question that doesn't seem to have a good high loading in any of the factors. So maybe we would go back and make sure that the wording of that question is good, or we might just want to throw out that question altogether. There's also a couple of questions for perceptions of our brand that didn't seem to do very well. A gain, usually you do very careful selection of your questions. You would go back, read what were those questions, is there something we should tweak, or shall we just get rid of them? Now, for the time being, the feature I want to highlight is that under the red triangle menu of this model, there is a new option for copying the model specification for SEM. I'm going to click that. W hat it does is that the loadings that are bold here in our final rotated factor loading, those loadings are going to be stored so that we can use them in the SEM platform. Normally, you'd want to collect a new independent sample so that you can confirm these exploratory results. Now, let's just assume for a minute here that this data table is my new independent sample, and I would now go to Analyze, Multivariate Methods, Structural Equation Models, and I can use all those same variables, like on model variables, and then Okay to launch the platform. Normally, again, you want to confirm the results that you found with an independent sample. What you can do is in the main red triangle menu, you can click on paste model specification. N ow notice that the factor loadings from the factor analysis platform were rescaled by the standard deviations of the indicators. I'm going to click Okay, and you can see now that the values here are fixed for the loadings of those late in variables. They're fixed to correspond to the values from the factor analysis platform. Now, again, they have to be rescaled beause the variance of the variables is taken into consideration. That's the proper way to specify the model. But it's really nice to be able to streamline this workflow because normally, if you really want to fit a confirmatory factor model based on an exploratory factor analysis, you would have to put these constraints by hand. T hat's really tedious. So we've made it very easy. These latent variables have loadings that are fixed to known values from a previous study, from a previous exploratory analysis, and we can now confirm whether or not that factorial structure still holds with a new sample. One thing I should clarify is that the three variables that did not have substantial factor loadings in the report are not being linked to any of the latent variables. R eally, we don't want these to be here in the analysis. W hat I can do is the red triangle menu also has an option for removing manifest variables from the analysis. I'm going to use that so that I can quickly just find quality 3, brand 3, and brand 5 a nd I can just click Okay to get rid of those variables because I don't really want to fit my model with them in there. A gain, now I can just run this model, assess the fit, and figure out whether I can, in fact, confirm my results from exploratory factor analysis using confirmatory factor analysis in SEM. T hat is all I have for this demo. I hope that this is helpful and I look forward to answering all your questions during the live Q&A. Thank you very much.
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Navigating Your Data Workflow: Workflow Builder Grants Your Wishes for Data Cleanup (2023-EU-30MP-1262)
Saturday, March 4, 2023
What if you could save time in your process of collecting data, cleaning it, and readying it to begin your analysis? Accessing data and preparing it for review is often the most time-consuming part of creating a new data analysis or project. With that in mind, we would like to introduce the Workflow Builder in JMP® 17. With this exciting new feature, JMP users can now record their entire process from beginning to end, starting with accessing data from multiple sources. Working with the action recorder (added in JMP® 16 to track steps and provide scripts that can be saved and reused), Workflow Builder tracks all your changes in data prep and cleanup, data analysis, and reporting. In this presentation, we will show how to operate the Workflow Builder, save each action, and then replay and share them in a polished report. This is sure to become your new favorite feature in JMP 17. No manual cleanup means extra time in your day! Hi, I'm Mandy Chambers, and I'm a Principal Test Engineer in the JMP Development team. I want to talk to you today about navigating your data workflow. The Workflow Builder is new for JMP 17, and I think it can grant all your wishes for your data cleanup. The Workflow Builder is the ability to record your JMP data prep and analysis workflows. It tracks all your changes that you make, cleaning up and reporting. It records your steps and you can use them over and over again. It allows you to save your data and your workflows, package them together nicely, and share them easily with others. It also is just going to save you lots and lots of time in your day. This is a screenshot of the Workflow Builder. It's listed underneath the File menu system in JMP, File, New, New workflow. You need to open a data table or import data to begin and you will be prompted with a question that says, Do you want to record? When you say yes, then the screen on the right will show you that, like in this case, we opened Big Class and it will be recorded. I'm going to get into this today and show you as much as I can. You can see here there's lots and lots of stuff happening. There are step settings, there are navigation buttons, there are images, and so much more. I probably can't hit everything today, but I'm going to do my best to try to show you. Let's get right to the demo and I'll get started. This is a new utility. I do think it saves you time and it allows you to do clicks instead of coding. I think that's really cool and I think for a new JMP user, especially, this is going to really make their life easy. Great job to JMP for building up ways that we can capture JSL. In 16, JMP introduced the Action Recorder, and now in 17, we give you the Workflow Builder along with even more JSL added to the Action Recorder. The Workflow Builder allows the JSL to be saved and the steps replayed as well as shared. A gain, no coding is really required unless you want to add that. I'm going to get right into this by showing you a workflow. This is a workflow that I saved as a demo. Most of these examples, I have tried to use sample data so that you can go back yourself and maybe try some of these. I'm using the food journal data table from JMP. I'm recoding a column, I'm changing a column properties order, and then I ran a Text Explorer report. You see the buttons here at the top, and this is a saved workflow. I'm going to just click this middle button which says to execute the workflow and run it so you can see how quickly it runs. Notice a couple of things here really quickly. Over here on the right, you see these little images pop up. A ll that is simply, if I click on that, is it's a screenshot of each of the steps. It doesn't really do anything. It's a screenshot. It just shows you what's in there. Again, if I click this one, this is the column where I changed the column property. That's nice to have in case you don't remember what you did. The little green check is the check that says the step executed properly. If it didn't execute properly, you would get a red X, so you would know something didn't work right. Now, I'm going to recreate this for us. Looking at this Workflow Builder here, I had created my local data filter is why I changed the order. I wanted it to be in this order. I hung out on this late snack thing because I was looking and thinking, "Good heavens, if I had cappuccino, moko, and chocolate, and candy, and sugar at night, I would never go to sleep." I don't know about you, but anyway. Let's close this up. That's this little reset button right here, closes everything in the workflow. Then I'm going to go up under the File menu system. I'm going to click New and I'm going to go down here and click New workflow. Here's my new workflow. Let's observe a couple of things here real quick before I get started. This is the little record button, as I said, so we'll put that... I'm not going to push that. I'm going to show you how that executes. But you have places where you can get data from. The JMP Log history over here, I had reduced it down just because I didn't want to see it while my workflows are running. But throughout the day, you could open the Workflow Builder and you could just use it all day long and it will stack your statements down here in increments of 10 minutes, an hour, two hours. It'll save things. You'll even have something if you left it up that would show that it was done yesterday. You could come back to this and you could grab statements and all you have to do is grab them and hit this arrow. You can push them up or you can grab them and drag them or copy and right-click, I believe, and copy and go up and paste them in. All sorts of ways to get things into your Workflow Builder. I'm going to right-click here and delete this. I'm going to cut it out because I want to do this the other way. Let's get out to my JMP home window and just simply open my food journal. There's the button I told you you would get that says, "Hey, do you want to record this?" I'm going to go yes. Notice that in the Workflow Builder right here, the little red dot is now hollowed out. I am in record mode, so everything I do will be recorded. I'm going to quickly go in here, so I can make sure I get through a lot of examples. I'm going to recode this and I'm going to go in and I'm going to do it in place because I don't want an extra column. I'm not going to type a whole bunch of stuff this time. I'm just going to convert this to upper case so it makes the change and I'm going to recode it. Then I'm going to go in and I'm going to go to my column info and I'm going to go down and add value ordering on this. Now I'm going to re order this to where I have breakfast first. I've got an AM snack. I need lunch to move up here. I need a PM snack to be after lunch, then I have dinner, then I have a late snack. There's my ordering I wanted. You'll see that those steps have been recorded. There's my open, there's my recode, there's my change of column property. Now I'm just going to grab this Graph Builder here is the one I think I read. That's easy. Then I don't have to recreate a lot of stuff. I'm going to add a local data filter and I'm going to use that meal column. Again, here's the order I wanted. If I click on this, I get a slightly different view. It's not a Text Explorer, but you can see here, here's the column for chocolate and of course, it has the most calories. One of my favorite things is chocolate. Anyway, notice that this Graph Builder is not recorded yet in the Workflow Builder. Platforms don't get recorded right away, but it records after you close the Graph Builder, or we added a button under here. If you want to save the script, you can save it to the workflow. But for all I'm doing today, I'm just going to close it. There it's been written to the Workflow Builder. The other place that you can go and grab stuff, and this is one of the things I love about the Action Recorder. If I wanted to, I can go over here and you can click on these steps as well now. Under this red triangle menu, you can save the script and say add it to the workflow. You could always go over there and get something if you needed to. Let's stop recording. Let's hit the reset button and let's close this thing. Now, let's run it and it should run exactly the same way as the first one. I did a little bit different platform, but you can see that column has been recoded and there's my Graph Builder. Now, we have created your first Workflow Builder. The first is as simple as that. Moving on to a second example I have. Let's look at this virtual join example I created. I used the Pizzas examples from the sample data library. If you're not familiar with virtual join. I'll give you a crash course here in a minute or less. But I opened the tables, I created link IDs, which you need with virtual join, and then I created link reference columns and I simply ran a Graph Builder. I'm going to run this and then I'm going to show you some things about it. I'm going to tweak it a little bit, show you how we can clean up the Workflow Builder. Here's virtual join in 30 seconds or less. A link ID is created in pizza subjects there. A link ID is created in the profiles here. Then I went to pizza references, which is what I like to call my main table with virtual join. A ll my references are set up this way. All of these steps I did do manually. It was all saved in the Workflow Builder and created, and then I simply ran this graph. That's that. Ways that you could clean this up as a presentation is if I was showing this to somebody, they don't really care about seeing the data tables and all that stuff. They would be more interested in my graph and maybe if I did a distribution or a tabulate, the reporting I would get. I'm going to show you a way to go in here and do some things on the right hand side here of the Workflow Builder. You open the step settings and this is where all the magic happens. When you're doing these things, the JSL is getting is saved. This is where it's happening and we have some additional buttons over here of things we can do. Like I said, I really don't care very much about the table showing, so there's a nice little option in here called hide tables. I'm going to click this and I'm going to go down and add hide tables to every one of these so that it quickly hides those three tables and I don't have to worry about that anymore. The other thing that's nice is that these steps right here are actions are all doing things to the data tables. I'm going to select these and I'm going to right -click and I'm going to say group those selected things together and it will put them in a little group. I'm going to name this something. They're going to always be called group, group one, group two so you might want to name them something that means something to you. I'm going to call them link ID and link reference. The nice thing about this is as you're building workflow, I can reduce this down. Your workflows could be really long. If I reduce this down, I have a lot more space. You can do groups within groups. That's nice too. Anyway, I'm going to expand this for right now. Then you probably remember when I ran this, it ran really fast. My final step is a Graph Builder, and I want it to hesitate just a little bit so you can see it hesitates. I'm going to go in here and add a custom action. This is where the coding comes in. If you do know JSL, I'm going to add a wait statement here, and I want the wait to happen before my step, so it's going to be right here. You do have the ability in this to use the arrows to push things down, to push things up. You have a little bitty trash can right here, so if you add something, you can delete it. You also can simply leave something in and uncheck it, and that actually works really well, too. I'm going to go in here and this is where because I know JSL, I'm going to type a wait statement just as simple as that. Let's close this up and let's run it and let's watch. There's the hesitation and there's the Graph Builder. Let me close it one more time and get you look right here and look for a little running man when I run this. I'm going to execute it. There's the running man and there's the Graph Builder. You can see that this is much more presentable if I'm doing something. Again, if I had other reports I wanted to show, that would work really well. A couple more things you can do. Obviously, when you create a workflow, you save it up here by saying File, Save or Save As and give it a name. Every workflow has an ending of a . jmp flow. If you want to create a Journal, the developer was cool in creating something called Add Steps to Journal. You get this right here, which is nice. Journals are nice, but they're hard sometimes to make. This is nice. Each step is in here. The code is in here. You could run it, you could clear it, you can look at it. I've got a thumbnail down here for my Graph Builder, and if I click this, I get the full -size Graph Builder. Really nice feature to add that in there. The other thing I wanted to point out is if you go up to the red triangle, you have the ability to save the script to the script window. This is the entire script for doing all of these things we've just done. Even the hide function is added at the top to hide with the tables. The only thing that you need to know is that this will run just like JMP would run prior to JMP 17 without a Workflow Builder. This script does not rebuild the Workflow Builder window. The only way you can use the window is through the UI. But it's nice if you want the script or you just want to save it that way, then you have that ability to do that. Moving on. The next Workflow Builder I wanted to show you was an educational type. Let's look at this one. This was done by Peter Hersh. He did something much more complicated and I took it and made one of my own. But basically, it's a workflow that opens a data table, it runs a distribution. It goes out to a particular section like right here, and it's using what we call a show message window, a model window where it stops. I put in a definition for quantiles that I took right out of JMP book because I don't know that stuff. Then I said, okay, and I did a second definition which is showing just a summary of these are the other stats that are listed in the summary statistics. Kind of a nice thing to do here. Notice a couple of things about this workflow. I'm going to close this up. Notice I don't have a red button. If I go here and say File, New, New workflow again, I don't have a red button and I don't have the JMP Log history. When I set this up, I put this in something called presentation mode. That's first thing under the red triangle. You'll see, if I uncheck that, those objects come back. But I didn't feel like for this thing, you actually need that in there so I turned it off because it's really something you're using for teaching. The other thing that you can also do in here is you can duplicate a workflow. You can just open a new one and this would be one where you could change something. Let's make a couple of changes here because I just want to show you how nice this is that this will be able to be changed. I'm going to go and get a different JMP table. I'm going to go get the Cleansing table. Then I'm going to show you how this is set up inside of the report. The report step code was to run the distribution, which I will have to change this as well to be the same table. Then I need to change the column that in this table is called pH. The dispatch for the quantiles will have to be pH as well. This was assigned, the distribution is run, but then it's been assigned. This is a little JSL where it was assigned to a report. Then the show message window, which you grab from here, this is how this is added, was added in here. You give it a title, which is the title of the message window is client tiles, and there's where I pasted the definition. Then your next step is to clear. It's another JSL step to select your report and deselect it. Then you go on and select the second section of the report, which is the summary statistics. I need to change this again to pH. Then at the end, here's my second show message window that was added. You can add as many of these as you want to. It's a model window so that it will stop and wait for you to do something. If I've made these changes correctly, let's run this. There you can see there's the Cleansing table, there's the pH column with the distribution. I'm selecting the definition of the quantiles, and I'm going on and using the definition for the summary statistics at the bottom. A really nice feature here, really cool thing that you can actually do with teaching, I think. I think there's going to be a lot of people that enjoy using that. Workflow subset is also a really cool thing. You can create a subset of a workflow by going into here and selecting temporary subset, or you can actually do it on the right hand side where you open a table, you add an action, and you say subset in here, which is what I've already done with this. Here's my subset. You get this little window whether you do it from the temporary side or whether you do it here and it's stored right here always, just so you know. But we built in some selections here. Your selections are to get all the table, 50 % of the table, 25 % or whatever, and that's what I've checked. These are choices. This is going to make a lot more sense for a table that's really big. This is great because you can run some analysis on some of the table, maybe that's big. Then when you're all done with capturing everything, you would go back and maybe change it back to all of your data and it should work seamlessly. What I did here was I'm using something everybody's probably really familiar with consumer prefs, and there's about 446 rows, so I'm using 25 %. I'm going to run this and I just ran a categorical platform. You can see here, this is the output from this on just 112 rows. That's all it used in this case. Now, I'm going to close this up and go over here and say, "Okay, let's just say I'm done with what I want to do and I don't need a subset anymore." Well, I can go back here and delete this with the trash can, or I could simply just uncheck it this time and basically run it again because I might want to do something later with it. Now when I run it again, you can actually see right here that you get the 448 rows. Okay, that's how many are in there. You get your full platform, you get all the data analysis. I think people are going to get a lot of mileage out of that as well. Being able to do the subset, especially if you're using millions of rows of data, I think that will be very, very helpful. One more thing I forgot just then. Let me show you quickly how to create a workflow package. If you go in here, last thing in the menu is to create a package. The difference in saving a workflow locally and creating a packages locally, it's for you. So it's going to go wherever you put things on your drive, on your Mac or Windows or wherever. But when you create the package, it creates a little temporary place and you can package it together and send it to somebody and it should work seamlessly for them. What this is telling me is that I need to have this data source of consumer prefs attached to this for it to work. There's that little button I told you about that said presentation mode. If you were giving it to somebody and they didn't really want them to change anything, you could check that or you don't have to check that. Let's say, okay, and I'm going to name this workflow package, just let it default. It adds the same . jmp flow to the end. I'm going to save it here. Then what I want to do is go back and hopefully I can find it. There it is. I'm going to open it and I want to show you real quickly how it saves that. It did do presentation mode, but just so you can see, here's the temp consumer prefs. Just to prove that it runs, there's this and it runs the same way. Really nice feature. Packaging is great for sharing stuff with other coworkers, sending reports and that thing. The generalized workflow is another really nice utility that I think people are going to use. This particular example starts out by not opening a data table. It was built with a template data table and and I ran a distribution. If there's things you like to do every day, like let's say you run a couple of distributions and maybe you run your favorite Graph Builder and you're always doing the same analysis over and over in a given day on all your data, this is the way to set this up. The way you set this up is you go under the red triangle menu to References, and you click this little button that says Manage. Now I'm in this Managing window and you can see here this is where I use this ANOVA template JMP table. I typed in this prompt that says select the table that I want to use for analysis. I asked the mode to be every time I run this, I want it to prompt me. Then I had three columns in that table that I used for my three analysis variables. For every one of them, I'm saying each run, select a column for analysis. I'm going to be prompted for that. Then you save this and this reference is all set up for this particular workflow. Let's run this real quick and let's see. Here's my choose table thing right here. Let's go other. It's giving me an option to go out here and I'm going to go to my data table and I've got a couple of things I wanted to try to show. I'm going to grab hybrid fuel economy here. Here's my prompts, what columns would I like to use? The first one I want to do is combined in BG and then I want to go and grab engine and then I want to grab the number of cylinders. Here's my three distributions, and then I've got my favorite box plot. That's just showing one. Just for fun, I wanted to show you a second one. Again, hybrid fuel economy is open, and so I'm going to select other, and I'm going to go to the sample data again, and I'm going to look for Titanic passengers and open this. Then I'm going to go in here and select I want age, and then I want sex, and then I want passenger class. A gain, here's my favorite box plot and here are my distributions and everything ran exactly like I wanted it to. Really good thing to use reference manager in order to build a template out. Notice when I close this, this didn't close the tables. The reason it doesn't close those tables is because those tables, there's not a statement in here that says open a table. We opened those tables from the file menu system, so I'll just simply go up and close those myself. The final example I want to show you is something that is Workflow Builder. We had some comments about people wanting to use workflows for archived projects. This is a project I did back in 2015, 16 when I started with JMP, and I think I showed this another time and mentioned that I had been working six years, and I went back the other day and thought, Wait, that's like eight years. I can't count, but other than that. But this is something where I went out to a website, I read in data on nesting in North Carolina, and it would continue to change. You would read it throughout the summer and I did some work down at Oak Island, which is a beach where we go often. Anyway, I had saved all of these scripts in a folder that I worked on. I thought, I'm going to try Workflow Builder with this and see what happens. Basically, instead of opening a table first, what I ended up doing is I went out and I used the custom action field where it says add custom action under here as my very first step. My first few steps, I went and took my script and I pasted the script I had saved inside of this window. This is what's in here. At the bottom, I'm saving a table. The data that I was actually using in this, all I had to do was go to the same web page and change it to 2020, 2021, 2022. That's what I did. Then there's a turtle species table where you go and do the same thing. Basically, I read these tables in and then I went to the Workflow Builder and I said, Okay, now what I want to do. Once I got them in, I started building my steps. I concatenated the table. I thought, well, I don't need those original tables, so I closed columns so they get closed. I added some columns. I created a change here. I hid and excluded some totals I didn't need and then I ran my platforms. I'm going to run this so you can see. The cool thing about the platform runs that I'm going to show you is that these are scripts that I didn't have to change at all. As long as my columns were named the same thing, I just took the scripts and copying and pasted them right into here and they worked beautifully just like they did eight years ago. Notice in this workflow here at the bottom, these two steps right here are italicized and grayed out. The reason they're in there is because I didn't want to show them today. If I right -click on this, you'll notice that the step enabled button is not checked. I'm going to check it just so you can see that change. Now that changes so that it's dark and it's like everything else. A nice feature that you can do this because as I was looking at this, I thought, "Well, I don't want to show this today, but I might want to keep them. I don't want to delete them." This turns it off so it won't run, but I don't have to necessarily get rid of it. Let's run this. Pay attention at the top. This is a good example to watch the little running man that pops up here. It takes a minute. You see him because they're going out to the internet, it's actually getting data. This little part goes a little slower, but then it takes off and goes very fast. But once the data is in here, then everything else runs really fast. Really nice that script all worked. Here's my data. I have this turtle nesting data showing May, June, July, and August for each of these years, 2020, 20 2 1 and 2022. Nesting just for a case study and point tends to trend up, and then the next year it might go down. Again, it did that. If you go back and look at data from way back, it's just up and down, up and down. But 2022 was a pretty good year for nesting in North Carolina. Then this was a really cool graph that I had before that I loved. It's a bubble plot, but I went out and grabbed a turtle SVG file and plugged the turtles in for the bubble plot. This is showing nests totals with false crawls, and just false crawls are when a turtle comes out to lay her eggs and then she's scared or something and she doesn't lay or she changes her mind. They're usually pretty even numbers on a certain beach, they'll be pretty close to each other. That's why this bubble plot is cool the way it works. It's showing the nesting totals with the false crawls. I'm interested in this turtle right here because that's Oak Island where I said that I go to the beach and I did some work down there with them back in 2015, '16. Kind of a fun graph. Kind of neat that this is an archived project and it actually works now. It's a good way to record and save stuff. That's all I have today. I just want to say that I want to thank the development staff for working so hard on designing this. Hernes Pessour, David White, and Evan McCorkle, just to name a few. Julian Paris was instrumental during the design phase and advisement of this. I do have a reference down here for the sea turtles if you're actually interested. In closing, I just want to say I think Workflow Builder is the best new feature for JMP 17, but I'm a little biased, but I do believe it's going to save you time with less coding and more clicking. I think you're going to get more and more out of reusing recorded and repetitive steps. It should simplify your work efforts, and then it will definitely accelerate your daily processes, leaving you much more time in your day. Thank you for listening today. Thank you for letting me share with you about the Workflow Builder. Please try it out. Please let us know what you think and we'll look forward to hearing your feedback.
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Automatic Data Refresh in JMP® Live 17 (2023-EU-30MP-1194)
Saturday, March 4, 2023
In past Discovery talks, we've shown how to acquire data, create a report, and publish it to JMP® Live using the desktop task scheduler. But what if your JMP Live report does not change daily? What if only the data changes, and you want to share those updates with your colleagues? JMP Live can now schedule the refresh of your data on JMP Live without having to publish your work again. This presentation will show how to use this new capability and discuss this new feature in the context of JMP and JMP Live plans. 2023-EU-30MP-1194 - Automatic Data Refresh in JMP Live 17.mp4 My name is Brian Corcoran, and welcome to Automatic Refresh of Data in JMP Live 17. I'm a JMP Development Manager, and my group is responsible for JMP Live. What is JMP Live? For those of you who may not know , it may be worth giving you a little introduction. JMP Live is a web- based collaboration site, so users can publish reports from JMP, their desktop version of JMP, and the data to JMP Live. Users at JMP Live can interact with those reports, and if they have JMP, they can also download them to work on those reports with their desktop copy of JMP. A copy of JMP is not necessary, though, to use the JMP Live site. Now, in JMP Live 15 and 16, we required users to publish new content from the desktop application in order to update things. A common request that we got is that it'd be nice if the server could do this for me when I'm not working or whatever, and I could just look at an updated copy at my leisure. That forced us to revisit how we treat data in JMP Live 17. JMP Live 17 really represents a major rewrite of the product. We made data an equal to a report. Before that, it was along for the ride hidden, and you really wouldn't see it being transmitted up to JMP Live. Now you can publish data independently. You can look at it on the JMP Live site. You can update just the data in any reports that use it, and they can share that data will all automatically be recreated with that new data. This work was all done to provide the foundation for refreshable data. Here, the contents of the data post on JMP Live are refreshed on the server side, and there's no intervention by a user in the JMP desktop client. Usually, data of this nature is in a database or some REST- based web endpoint or something like that. It has to be data that is accessible from the server where you have JMP Live installed. JMP Live provides us, we hope, easy to use scheduler, so you can put in a repeatable, hands- free refresher of your data. This fulfills the dream of, I go home at night and the data is refreshed, the reports are regenerated. When I come in the morning and I'm drinking my tea or coffee or whatever, I can look at the updated report and make decisions based on that new data. I'm going to provide a variety of scenarios on how you can learn to do data you fresh with JMP Live. Let me first shut down PowerPoint, and I'll bring up a copy of JMP Pro 17, but this will work on regular JMP equally well. All right, so first , I'm going to start with a really simple explanation of how we separate reports and data in JMP Live 17. I think it's important to understand that before we proceed to the more complicated examples. I'm just going to bring up a sample data set that we ship with JMP, the financial data. It's just data for 500 companies across a variety of industries, and you can see them organized by type. It's basically a sales profitability data number of employees, things like that. Let's suppose that I create a simple bivariate plot of sales by number of employees . There's not a lot to it, but I can hover over the points and see the data. Let's suppose I want to publish that to JMP Live. I'm wanting to go here, and I'm going to say publish report to JMP Live. I've set up a connection to a server that's internal to SaaS and that has access to SaaS resources here. I'm going to publish this as a new report. I've set up a folder called Discovery to publish this report into. We'll just go ahead and do that. The first time it makes the connection, it can take a little bit longer, but there we go. Let's go ahead and bring up this JMP Live release. I'm going to go to my space that I just published to, Brian Corcoran, in my Discovery folder. There is my report. If I open it up, you can see I can hover over it just like I did in JMP. But I'm looking at this, and it's a boring report. There's not a lot to it. Maybe I should have included some more information. Well, let's go back to our report. There we go. Let's suppose I want to add a fit mean and a fit line to that. I also want to supply a local data filter. I'm going to make a filter by industry type so that I can cycle through each industry and look at the individual companies involved. If I get the drug pharmaceutical companies, I can hover over the one with the most sales, and we see it's 9.8 billion. Now, let's suppose that I want to update this report. But in the meantime, maybe I've got information that says, "Hey, this is not a $9 billion company. It's a $19 billion company, but I'm waiting for verification on that. I don't want to publish the data with this, but I really would like to update the contents of my graphic. Well, we can still do that. I'm going to go ahead and publish the report. But this time, we're going to do a replace operation. We'll select replace an existing report down here. It's going to ask us, what report do you want to replace? It's going to give us the candidates like the most recently accessed, and so there's our financial report . I'm going to say next there. Here it says, "What do you want to do about the data? " I'm going to select, Use the data that's already up on JMP Live. I'll say replace the report. It goes ahead and does that. If I go up to my site, I can see that it did indeed add my fit lines and means and my data filter. I can manipulate these like we do in JMP, but you'll see that my outlier company still is a $9 billion company. All right, so we did not update the data. Now, let's suppose I've shut this report down, but I do get information that the sales are indeed $19 billion for drug company number one. I can choose to publish just the data. I'll say, Update existing data. Once again, I'll select my financial post, and I want to replace that. Now you see that it's automatically reloading the data . It's going to recalculate all of our statistics down here as well with new fit lines and that. Now we can see that our outlier is represented here as a $ 19 billion company. If I wanted to, I could even bring up the data with the data viewer that I mentioned earlier , allowing us to explore that, and there is our update to our drug data for company number one. All right, so that is the separation of reports and data, and that provides the foundation for our data refresh. Let's go ahead and get into a real refresh example. Let's minimize this for a minute, and do a little cleanup because otherwise, we will get confused where we're at. Now, my next example will be a simple data refresh, and it allows me to introduce another feature that's new to JMP 17, and that is access to the OSI Pi Historian database. If you're not familiar with historian databases, they're often used to collect lots of process data, maybe from manufacturing lines, lots of different machines and devices, putting real -time data into a database. Then you can look at this historian database at your leisure to analyze trends and see where there are problems. We have a historian database with sample data here at SaaS, and I'm going to select that and get some information out of that. Here is our connection to the PI server. I'll open the sample data area. What we have here is a simulated data center where we have lots and lots of racks of computer servers. Essentially, we're looking at power consumption on all of those to see where we're spending a lot of money and things like that. All of these represent a table, and we can import all of these at once if we wanted to. I'm just going to import the data for power meter number one here. This is old data. I'm going to go back in time on it. I'm going to ask for 5,000 points. I'll take just a second or two to import, but we'll start that up. There's our data. I would call your attention right here to this source script. This is important. If we edit this, we'll see that it contains information to recreate this data fetch, including the location of our P I server, the actual table here that we want to import, how many points we want. This will be useful. Let's go ahead, though, and just create a simple run chart and control chart. I can select that, and we're just going to do the values. All right, so there it is. Let's go ahead and publish that to our JMP Live server. All right, I'm going to publish that as a new report back into our Discovery folder. I'm just going to publish it as is. Close that, and we'll bring up our browser. We are going to go to a different session. Hold on. We are going to look at my Brian Corcoran space again. In the Discovery folder, we'll find our Atlanta data center. Now, we can open in that up and see the points and all that. We know how that works. I'm going to call your attention to these Files tab, though. Here we have our financial report. But here's our Atlanta data center report. This is the report, but this is the data table, so let's click on this. There's the report that's based on the data if there are multiple reports that I'll show here. Here's the settings, and here's where it gets interesting. There is our source script that we had down in our table in JMP. Here's something called a refresh script. Let's just concentrate on these two panes now. The source script has been uploaded with the data, and it provides us a basis for how we could recreate this data. A refresh script is a piece of JSL that is going to supply data when a report records it. There's one big rule for a refresh script, and that is that the last operation that it performs must be to return a data table. Essentially, data refreshes are done through JSL scripts. Let's enable this refreshable button. Let's copy this script as the basis for our refresh script. I'm just going to paste this in here. If you remember earlier when we were looking at the P I dialog, I said you could import all kinds of tables at once . Because of that, OSI PI import JSL returns a list of data tables, not a single data table, but a list. Our rule for a refresh script is it must return a single data table. We need to get to that point. I'm going to assign this output of this refresh script to a list variable that I just arbitrarily named DT list. I'm going to put a semi colon on this run statement. Now I am going to assign the first element in that list, which is the only table that we have and the only table we care about to a variable named DT. Since that's the last operation in the script, that's what's going to be returned here. While we're at it, though, why don't we go ahead and we're going to change it to return 10,000 points. I'll save that script out. Now, let's go ahead and try this out. Here's a button we can manually refresh our data server side. I'll say yes, we know what we're doing. We want to do this. All right, I said it was done three minutes ago, and then it changes to a few seconds ago. Let's look at our report. Here's our report. Let's look at it. It looks a little different than the one we had back in JMP because now we have 10,000 points of data. We've done all of this on the JMP Live server, not on the desktop client. Now we could recreate that on the server , if we want, without ever having to involve our client. Let's go ahead . That's our first example of data refresh. I'm going to clean this up, so we don't get confused with our other work. A common operation where you'd want to do a data refresh is a fetch of data from a database. One of the big ones is Postgres, and I'm going to show an example of fetching from Postgres. Before I do that, I'm going to go ahead I'm going to change the JMP Live server that I'm accessing. This one is actually outside of SaaS resources. I can go to manage connections here. You can actually look at this yourself. It's devlive 17.j mp.c om. If you go out there, you'll be able to see these reports. For this one, I'm going to bring up Query Builder. On Amazon Web Services, I have created a Postgres database full of sample data. I'm going to open that up. I have some stock quotes for Apple computers that I just put up there for demonstration purposes. Let's go ahead and open that up. I'm going to just build a query quickly. I only have date and quote data. This is essentially the closing quote of the stock price for the end of the day, and it starts on the January 1st of last year. Let's go ahead and just run that query. You see what I'm doing here. Let's take this a little bit further. I'm going to shut this down. Now, another new feature of JMP 17 is something called the Workflow Builder. I'd like to integrate the Workflow Builder into this demonstration tool. Let's do that. I'm going to go over here , and I'm going to say New workflow. It's going to capture my actions as I do them. I'm going to start recording that workflow. Let's go ahead and we're going to run our query again, this time with the workflow recording it. It'll capture that query. You see here, if we look at our source script again, once again, this has information on how to connect to that Postgres database, the table that we want to look at here, and what the query actually is. Now, let's suppose after the fact that... I don't want that. Let's see. Let's suppose after the fact that we decide we want to do some manipulation on this table. Maybe I want to do a subset of the table based on a where clause. Now, I probably could have done this in Query Builder, but I thought of this after the fact. Let's do row selection. I'm going to select where, and I'm going to select where the date is greater than or equal to June 1st of last year. I'm going to make sure that it's selected. There it is. Then I'm going to do a subset of that table just using the selected rows. Then maybe after that fact, and you'll notice that our Workflow Builder seems to be accumulating this information, I'm going to go , and I'm going to color by column . I'm going to color by this start quote. This is the data to the point that I feel like I want to do my analysis. Let's stop the Workflow Builder from recording that. Let's go ahead and create a graphic on that. I'm just going to do the quotes by the dates. We see when the stock was high back here in the summer of 2022, and it's gone down considerably. Let's go ahead and publish that. Again, this is going to a new server this time. The first connection can take a little bit of time sometimes. All right, let's publish new. This time, I've set up a space called Discovery Year of 2023 with a folder named Automatic Data refresh. You can look at this at your leisure and see this yourself. I'm going to name this Apple Stock Quotes Since June. I'm going to go ahead and publish that. Let's go ahead and look at this server. All right, and this one's in dark mode. Make you realize you're on something different. Here's our report. Let's go ahead, though, and look at that space, and I can search on Discovery to see which one I want to look at. Here's Europe. There's my folder, Automatic Data Refresh in JMP Live 17 in our report. L et's go ahead and look at the files, though. There's our Apple quotes, and there's no source script. What's up with that? Well, let's go back and look at our data here. We look at the source script here . We see that subset operation just picked out individual rows that were selected. I t couldn't go far enough back to understand that this came from a previous database fetch. It just knows it has this table that had been unsaved at that time, and it was picking rows out of it. That's not going to be helpful for us. We have our workflow and I've stopped that. Let's go ahead and say, what happens if I say, save script to script window of our workflow? There it has captured our query along with our subset operation and our coloring operation. Let's go ahead and we're going to copy this and use this as a basis for our refresh script. All right, so let's make this refreshable. We'll go ahead and edit. All right. This does require a little bit of change. First of all, I'm going to return my information to data tables. This is my query data table, the original full query. I don't really need to, but I'm going to assign that to a variable here because it provides clarity for me. Here's our where clause selection . This is what I really want to capture this subset operation. I'm going to say this subset, and I'm going to put brackets around here. The reason is this. The way Workflow Builder builds this is it cascades or chains together operations, and we have a select where here. It's going to take the most recent operation and assign it to my variable. I don't want the selection to go into this subset, I want the subset operation. I put a bracket around this part to make it just one object that's referred to by subset. It'll be the subset operation operation that goes into this variable. I'm going to go ahead and put that subset table in here for coloring, and then I'm going to put in an empty reference at the end here to our subset table. A ll that does is ensure that the last operation is to return that subset table. Let's go ahead and save this. Let's see if it works. Always good to test. It looks like it did. If it didn't, we could go to the history here, and we can see we did an on-demand data refresh. If it had failed, it would indicate that here, a nd the details pane, which just shows that we got a table back out of this, would instead show an error log from JMP itself. There's a hidden JMP session behind here doing the query for us, and it would provide the log of that JMP session here, so we could get an idea of what's going on. It looks like we have a valid refresh script at this point. However, I just essentially manually refresh data that I already had. That's not particularly interesting. Let's also look at this table. Notice that for the query information, it put in a holder for the password because we don't want to transmit those credentials. Most of the time, it also put in one for a user ID. In this particular case, it did not because Workflow Builder doesn't do that. But if you did this directly from Query Builder, it would. How do we provide the password, though, for this script that's going to need credentials to really fetch new data later on? We validated that this script is going to be okay, but we need the password for new data. What we do here is we're going to use this as sign credentials, and we have this credential that I have created, but I'll show you what you would do if you had none. You create just essentially a username and password pair and a stored credential name. In this particular case, I already had one , and it did get used, but I'm going to select this radio button anyway to make sure that we understand these two are associated. If we had more than one, we would need to have one selected for it to work. W hat's going to happen is my username and password, when it finds PWD or UID, if there's this placeholder with percent in, it's going to substitute those credentials in at the time of the query, so o nly when needed and make the query itself. These credentials are stored using secure string technology in the database, which also can be encrypted. They're very secure, and they're only used in memory at the time of the query. We're pretty sure that we're not going to have our credentials breached. Now, what do we do as far as creating a repeatable operation where we don't have to be around to do this? Well, we use the refresh schedule and hit Create. This is pretty flexible but hopefully easy to use panel. Right now, it's saying it's going to create a schedule that's going to run on every day. I don't want it to run on Sundays and Saturdays. I'm going to exclude those because the stock market is not open. You can have a repeat on a timely basis, like up to 5 and every 5 minutes. I only want it to repeat once a day, so I'm going to turn that off. When do we want it to start? If all your servers are operating in your same time zone, then you don't have to worry about this. You would just put in whatever time you're at, well, put in the time you want it to run. I have to have a little more complex calculation i n my case. I'm running on Amazon Web Services, and we run all of our servers on the UTC universal time, which at this point is 5 hours different. Because I want to show this operating quickly for a demo, I'm going to essentially take the time of my demo and add 5 hours to it and we're going to run that. We're going to put this in as running at 7: 31 PM. When I say okay and save this, it's going to calculate when it's going to run, and it says it's going to run in about a minute. That's what we are hoping for. Off screen here, I have an update to a database. Let's just pretend that this was done from some operation that's automatic, but I'm going to actually provide an additional stock quote that shows the stock jumping up in price. Maybe I'd come in in the morning, take a look at my new graphics, see the stock went up, and then maybe it's time to sell. We're just waiting on this at this point and hopefully, you'll see that it gets queued up and our report will get regenerated quickly. While we're waiting on this, I will mention, too, that your refresh schedule can be scheduled to terminate at a certain time. If you want it to end at the end of the year or something like that, you can put that in as well. We saw it refreshed a few seconds ago. Let's go take a look at our report. There's our report. We see it's declining price, and then if we hover, we see that we have a new point here, and it has indeed jumped up in price. That same script, if we go back here, will run 5 days a week, every day at the same time without our intervention . We've provided our credentials. Everything is automatic at this point , and we realized our ambition to essentially just be able to come in and get a new report every morning without having to worry about anything else. You set it up once, run many times, and we're good. That is probably the most important example I'm going to show. I'm going to show one more trick, and it's more of a trick that may help you in certain situations. Let's clean this up. What I'm going to do next is I am going to show using a REST endpoint. You're not familiar with that. Essentially, a lot of organizations make their data available through what looks like a web URL. I have a script that I developed, and I'll describe that in a second. Often you would need a credential or some thing called an API key or something like that to essentially have permission to use this site. Many of them cost quite a bit of money. This one does not. You access this URL, and it returns a whole block of data. JMP has a variety of methods that it provides to help you parse this data apart and put it into a data table. That's what we're going to do here. This particular site is one called Eurostats. It has a free API I was able to use. I'm not going to go into it, but it essentially has this query language that you can append to your URL to tell exactly which table you want and what data points . I have it starting in 2021 and not ending, s o we'll continue to get new data as it becomes available. It returns in this one big block of data, and JMP knows how to interpret that and turn it into a data table here. The data for the date information comes in as character, and we don't want that. We want numeric, and we want it to be continuous, so there's a little loop here that runs to change the column types after the import. If I run this , and what we're fetching, by the way, are natural gas prices in the EU. This data is pretty old. I don't know how often they refresh their data, but right now , they're only providing data for the first half of 2022. Hopefully, they'll update at least with the second half early soon. What do I do? I can look at this column data, and the columns are named specifically for periods of time. I really just want to have a periodic refresh of the data where I just grab the latest one and use it in a report. Let's look at this. If I were to do a graph builder, this is really handy, though, you can drop this in. The geographic and time period column can help you map out the map of Europe. If I drop in the latest one, unfortunately, there's some missing pieces, but it will give us a general idea of what gas prices averaged in this first half of 2022. Again, if I were to save this out, you'll see that it's going to use this date specific column. That's not what we desire. A little trick you can use here is to just rename the latest column. Here I have another little scriptlet, and I'll open that up. Essentially, all I'm going to do here is I'm going to say, let's take the last column and we're going to just rename it most recent. Then if I want, I can create a graph builder script that uses most recent. If I do this, there's our column. Now I can bring up Graph Builder and plop in our geography column, our most recent data. I can maybe enlarge that a little, and we can go ahead and publish that. Let's go ahead and we're going to just put that in our folder under the name Gas Prices in the EU. I'll publish that. We'll refresh our web page here. There's our gas prices graphic, just like we hoped, and it'll have, if we hover over it, the mean gas price and things like that. If I look at this data, though, and we go into our Refresh settings, it didn't understand all of our script about getting our REST endpoint and things like that, but that doesn't matter because we already have our script. Over here, I can just copy this as is and put it in here. We will return that table as our last operation. Maybe I just want to run this one day a week or something like that just so that whenever we finally get one, we periodically review this, and we'll see the latest thing. This is a case where you're just occasionally viewing the data to see if there's any updates. Of course, we'd want to make sure manually that this works before we move on to other things, but we can see that indeed it did the report, even though it has been updated a few seconds ago . Even though the data is the same, we know it's working so that when new data does come out, we will grab it and populate this graphic with it. A gain, this is on devlive 17.j mp. com. You will need a SaaS profile ID to log into the server if you want to look at this, but I will leave it out there for you to take a look. That it concludes our data refresh examples. I hope this gives you an idea of some of the powerful new capabilities that JMP Live provides. I appreciate you attending this talk. Thank you. Transcribed with Happy Scribe ShareThis({ sharers: [ ShareThisViaTwitter ], selector: "article" }).init();
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My Gauge Isn’t as Good as It Could Be—Will Its Errors Cost Money? And How Much? (2023-EU-30MP-1277)
Saturday, March 4, 2023
All gauges have errors. They might be minuscule, or they might be large, but they always exist. Large or small, the errors lead to gauges having some likelihood of making Type 1 and Type 2 errors (passing a bad part or failing a good part). The mistake likelihood is higher for parts that lie near the specification limits. These errors cost real money! But how do we quantify those costs? This paper builds on the results shown in a 2022 JMP Americas Discovery paper (2022-US-30MP-1123) that discussed how to quantify the gauge performance and how to set “informative manufacturing specs” (or guardbands) to improve the gauge’s performance in segregating good vs. bad parts. In this paper, we extend the learning and script functionality as we discuss how to combine gauge characteristics with the costs of individually passing a failed part, rejecting a good part, and projecting production volumes. This gives insight into risk analyses, e.g., how much I should budget to account for gauge errors, whether (or how much) to spend on improving our gauge, etc. Hi, I'm Jerry Fish. I'm a support engineer with JMP, helping customers in the central part of the United States. Today's talk is entitled My Gauge Isn't as Good as It Could Be— Will Its Errors Cost us Money and how much? I'm Jason Wiggins, also a senior systems engineer, and I support semiconductor users in the Western United States. This talk is a follow on to one we did for discovery Americas in 2022. In our first talk, we introduced the notion that measurement systems are integral to our businesses. In fact, we have many measurement systems we interact with in our daily lives. Along with that idea, we introduced the notion that measurement system or gauge variation can impact decisions in real world inspection situations. We introduced gauge performance curves as a way of visualizing gauge variation and relative to specification limits. In this talk, we'll extend that and explore the costs associated with gauge variation through a fun role play conversation between a quality manager of an automobile manufacturing plant, that'll be Jerry, and I'll be acting as a quality consultant. To kick things off, I'll get on a quick team call with Jerry. Hi, Jerry. Hi, Jason. How are you doing? I'm doing pretty good. Thanks for spending a few minutes with me. As a quality consultant, I help quality stakeholders like yourself understand and improve processes. Now, I prefer using JMP as it's a general purpose, easy to use data analytics package that has many quality and process control features. JMP makes quick work of the analytics part of process improvement, so more time can be dedicated to actually improving the process. Well, it is nice to meet you, Jason. Just to let you know, though, we already have software in place for our internal quality programs, so I'm not really sure what your software can do that we cannot already do. Can we make this quick? I understand completely, Jerry. I'll try to make the most of your time today. First, can you tell me a little bit about your company and your quality program? Sure, happy to. Acme Motors has built a reputation with our customers of manufacturing the highest quality cars. We're always concerned with quality. We have various gauges that we use to ensure our quality stays high. We've been doing this for years, and frankly, we think we're pretty good at it. I'm familiar w ith Acme Motors and your high quality reputation. My consulting team and I have recently been working with manufacturing companies like yours to advance the use and effectiveness of gauge studies. Measurement systems analysis, another way of saying that. One of the things we seek to understand are the monetary costs associated with the gauges used to measure process quality characteristics in your manufacturing plant. Have you quantified how much any of your gauges are costing your business? I'm not sure what you mean. Well, gauges are not perfect. They make mistakes. Sometimes they'll throw away good parts and sometimes they'll pass bad parts. Unless you have a perfect gauge and really no one has these, these mistakes are inevitable. I suppose so, but we've done gauge studies that say our gauges are good. Well, some of them are actually categorized as adequate by the AI AG guidelines. Doesn't that mean we're okay to use them? Well, possibly, but there is a lot more to the story than just using good, adequate, and poor AI AG gauge assessment criteria. For example, have you seen a gauge performance curve? I can't say that I have. No. Now, this is what one looks like. The X axis shows the true part values and the lower and upper specification limits are shown with these lines. The Y axis shows the probability of passing a part. If you have a part that is truly good but very close to the lower spec limit, there's almost a 50 % chance the gauge will recommend that you throw it away. That's one way of thinking about it. But also, there's nearly a 50 % chance that you will accept a part that is truly bad and near the lower spec. Very interesting. What happens if we could change the variation of the gauge then? Well, the shape of this curve definitely depends on how good your gauge is. Let's play with this just a little bit. What if we could reduce the variation by a factor of 10? Make a quick change here and replot our gauge performance curve. If this is possible, we will correctly accept or reject more of the parts. We're moving from incorrect to correct when we do this. Let me break from the role play for just a moment. The gauge performance curve I am showing is an add in that we made for our 2022 Discovery Americas presentation. The add in is available on the community. Back to you, Jerry. That is really an interesting chart, Jason. I don't think we do anything like that. What you're saying is that the gauge errors contaminate the measurements, but all I have is the imperfect measurement. Your gauge performance curve is plotted versus true part values. I wish I knew those true part values, then I could know exactly which parts to keep and which to throw away. Is there a way I can know the true part value? We could know that directly if we had that ever elusive perfect gauge, which we don't. We really can't ever get to the level of knowing the true value of an individual part, but we can estimate the true part distribution given our knowledge of the gauge characteristics and the measured part distribution. How would you do that? Well, if we assume that gauge errors are normally distributed, and for the moment, let's ignore any bias or linearity problems that you might have, and that we have the measured part distribution. If we have that, we can back out the variance of the true part distribution using a simple equation. That simple equation is just the difference between the measured part variance and the gauge variance. The plot on the right is shown for a situation where the measured variance is 25, the gauge variance is 16, and if we subtract those two, the true part variance is nine. We're beginning to get the parameters for that distribution because we know the results of our gauge study and we understand the variance associated with our gauge. Now, we would, from this, build a normal distribution that centered on the measured distribution mean with the new standard deviation. A gain, the result of which is going to look like the plot on the right. In the plot, the blue bars represent your measured part distribution. The areas above and below the spec are are shadeded in pink. Notice that the measured part distribution is much wider than the true part distribution. The measured distribution is what you get when you run the true part distribution through your imperfect gauge. Okay, that makes sense, at least a simple case. How do you relate this to what it's costing my company? Well, we can use this information in a numeric simulation to characterize the mistakes that our gauge is going to make. When we do that, we can generate a part inspection table like this. Let me study this table for a minute. My eye is immediately drawn to the center, the green box that says 95.4 %. Am I interpreting this right? 95.4 % of my total production is truly good and we're shipping it. Correct. That's a good thing. Now, looking at the first and last columns, if I add 18 and 25, let's see, that's about 0.043 % of my production parts are truly low parts. A nother 0.041 % on the last column are truly high. This is bad. It says my process is making bad parts that must be thrown away or reworked. I see another problem. If I look at that center column and I add those all together, I get 99.9 % of my production parts that truly are good. It says that the gauge is identifying 2.3 % of those as too low and 2.3 % is too high as well. Now, the customer doesn't care about that. They're still getting good parts, b ut I certainly do. I'm making good product and I'm throwing it away. Worst yet, look at that center row, those red squares. There's another 0.036, 18 and 18, of truly bad parts, parts that are too low or too high that are being accepted by this measurement gauge. This is serious. I do not want to ship bad parts to my customer if I can help it. That's right. This is cool. You're beginning to see the cost of having an imperfect gauge. This is really interesting. It shows that if I don't do something about my imperfect gauge, I'll risk accepting bad parts and throwing away good parts, both of which are bad for my business. On the other hand, I think we've got a way to handle this, Jason. Okay, what's that, Jerry? Well, we use something called guard bands. These are our bands that are set inside the specification limits. If we set them far enough inside the spec limits, we can reduce and essentially eliminate shipping bad parts. Doesn't that fix at least part of our problem? At least, guard bands are definitely a good way to reduce the percentage of bad parts that make it through your inspection process. A lot of companies use them. Have you considered the fact that improving quality using guard bands comes at the expense of throwing away good parts? Honestly, that has occurred to us, but we haven't tried to quantify that damage. Well, let's extend this example out a little bit more and let's just assume that we bring those specifications in by one unit of measure. We'll call these guard band limits. Our lower guard band limit would be 41 and our upper guard band limit would be 59. We're going to use this as our inspection screening values instead of the original upper and lower specs. Now, we can do the same numerical simulation and update the results. Let's just take a look at the differences between the tables. Can you see how the percentages have changed? We went from shipping roughly 0.04 % of parts that were truly bad to only shipping 0.03 % of bad parts. That looks successful. Maybe we could even squeeze our guard bands in further and improve that. Especially given our high production volume. We're talking real bucks here. It is. Also notice how many truly good parts are now being screened out. Every time you screen out and throw away good part, it is costing your company money. Well, you're right about that, Jason. Is there a way to look at this monetarily? What if we assume that a bad part in the simulation results in a bad car? Can we input the cost of scrapping the car and see how that affects the bottom line? Absolutely, we can do that. I'll need to get a little information from you, though. First, how much does it cost to make the car? Yeah, let's say for the sake of this demonstration, $35,000. Okay, great. That means for each rejected car, it costs your company $35,000. You might manufacture in rework costs here, but let's say, for example, we just throw the car away. Now we need production quantity. I don't know. Let's just choose a million cars. Okay, great. Now, how much do you charge for a truly good car that makes it to a dealership? The dealerships buy... Let's just say they buy these cars from us for $40,000. Okay. If I understand this right, your profit per car is that 40K minus the 35 K and your profit has been $5,000 per car. Right. Last thing, do you know the cost associated with selling a bad car? That's a little tougher. There are the obvious costs of repairs to the bad car or potential cost of return. Those are relatively easy to calculate, but there's also damage to our reputation. Our customers demand quality, and if we start putting bad product out the door, it can quickly get out of hand and result in lost future sales. That's a lot more difficult to calculate. I know you need the number. For the sake of argument, let's just say that totals to $50,000 per bad car that makes it out into the market. Excellent. Let's take a look at the profits and losses. Same simulation. Just review, make sure that we're looking at the correct values. You told me that manufacturing cost per car is $35,000. You then sell that to a dealer for $40,000. Our profit is $5,000. Cost of selling a bad car is $50,000. We're going to look at this across a 1 million car production run. Have I captured e verything, right? I think that looks good. All right. If we look at the net profits and losses, you stand to make about 346 billion from the 1 million cars you make. That sounds good. Not bad. The total profit from the truly good cars that are shipped is about 371 billion. The loss due to making truly bad cars that are caught in your inspection is 199 million, which is the sum of 98 million plus 101 million. Okay. The law... I let you digest for a second? Yeah, I'm following. Okay. The laws due to shipping truly bad cars, this is the one you are really concerned about, is 137 million, which is 68 million plus 69 million. Finally, the loss from scrapping truly good cars, this is what's costing your business, is $25 billion. That's quite a lot. That's the sum of $12.4 million and 12.4 million. That's fascinating and also a little depressing that we're losing that much money. If you change things, let's say you change the guard band settings, will the total net profit change? That's right. That's definitely true. You could see that change. In that case, then could there be an optimum? I can imagine widening the guard bands or narrowing them and looking at the net profit, would there be an optimum for that net profit peaks out? Yes, you can definitely explore that trade- off in a lot of different ways. You could answer questions like, how would improving my gauge by a factor of 10, like we showed with the gauge performance curve, improve my profitability? Or how much can I afford to spend on fixing or replacing a gauge? If we know what the costs to the company are for our measurement system, then we can justify the cost of fixing or replacing gauge. Also, just to your point, what if I adjusted my guard bands? We can definitely answer that question. A nother common one is what if I improve my process capability? I just tighten the variation in my process, what does that do to my profits and losses? I could trade that off against the cost of improving that process capability. Interesting. Well, I must say, Jason, I'm impressed. This has been a good use of time, but I think I owe it to my company to muddy the waters just a little bit. This is all great for normal distributions and simple gauge errors and those kinds of things. Those calculations that you've shown are easy. But what if I have gauge linearity or bias problems? Or what if I have a skewed distribution, which is really pretty typical in my company. We rarely run into the nice bell shape curve. Getting a true part distribution out of the measured part distribution becomes a lot more difficult than just using that simple formula you showed earlier. Can you even can do that? Absolutely. We are writing an add- in that will make you able to define the shape of any measured part distribution. W e can do the same exercise with measured part distributions that are normal or log- normal, uniform, Weibull, or even a custom distribution. It's an add- in we're working on. It's a work in progress. All right, that's fantastic. I'm ready to buy in. When will that be available? We have the basics of the add in worked out, but we need some time to make it more user friendly. We'll be working on that in the coming few months. Probably before midyear, we'll have that wrapped up. When it's done, we'll post it on the JMP website in our community file exchange. A few months, really? I'll forget all this by then. That's okay. We recognize that. Once our ad in is ready for prime time, we'll announce a series of open to the public seminars where we will go into detail about what you've seen here, as well as other aspects like relating these concepts to Donald Wheeler's EMP methodology, which is another personality in the Measurement Systems Analysis platform in JMP. Here's a quick peek at the topics for the up and coming talks. We'll spend more time elaborating on how gauge studies that are using the AI AG classification that we talked about earlier, we'll talk about how that can lead to unrealistic gauge assessments. We'll also explore how Wheeler's Evaluating the Measurement Process, the EMP method, can provide us more realistic gauge classification. We're going to present the problem with AIG and present the solution using Wheeler's methods. We'll also show how EMP method can advise us on how to use our gauge. How do we use it in the production process? One example that we'll be covering is objectively setting guard bends. The remaining topics, hey, we'll spend a little bit more time interpreting gauge performance curves, talk about how to blend performance with part variation to determine cost associated with imperfect gauges. Really, that is what we're talking about today, but we feel like we need to extend that a little bit so that we all understand how that works. Final two topics, how can Wheeler's calculations be factored into this gauge cost conversation, and how to understand gauge cost, again, to the point of non- normal part distributions. That's perfect. Can you make sure that I'm on that invitation list? I want to make sure that everyone in my quality department attends your seminars. Sure thing, Jerry. Anything else I can do for you today? Yes. Get back to work on that ad- in. The sooner it's available, the better. Will do. All right, this concludes our presentation. I'll say that as we were doing research for this talk, we uncovered many concepts that are important to understanding how to use measurement systems. We feel like one of these concepts deserve more time than we had in our talk today. We look forward to continuing the conversation with you in the coming months. Any closing thoughts, Jarry? Just that if you've kind folks that are attending today, if you're interested in attending those upcoming seminars, please let your local JMP support person know or your support team know, and they'll make sure that you're included on that invitation list. Excellent. With that, thank you, everyone, for attending. Thanks, all.
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Enrich Databases With Useful Information (2023-EU-30MP-1306)
Saturday, March 4, 2023
JSL can be used to make GUIs to access data in measurement systems with pre-programmed scripts, such as for SPC. For this to work, JMP® must remember the control limits from the status quo when new data arrives. A second JSL script is needed to connect to a DB and load existing limits for variables if they are present. Control limits can be altered/added manually and updated in the data table or automated with default values and a factor for multiple variables. When others pull the data again from the DB, the control limits are automatically added to the column parameter and go into the SPC. This presentation will provide a live demo and JSL example of how to insert and update data in a DB. Hello, my name is Mauro Gerber. I work as a data scientist for Huber and Suhner in Switzerland. I would like to introduce you to the problem we had regarding SPC and I would like to have you on the right. I want to talk about SPC scripting and why it's important to write the information back into a database. W hat we have is an optical measurement system that measures dimensions on parts that we store in a database. The goal was now to get the data back out of it and to statistical process control on it. Now what happened was that over time some variables can shift and the worst case would be that we get the out of spec and then we have to do some measurements to get it back in. It would be preferable if we can do that beforehand. The idea of SPC is like telling a dog to stay where it is. W e achieve this by redefine a stable face and say if the process moves out of that window, it gives a notification before it gets an out of spec and we can take measurements to get back into stable again. O ne way to achieve this is by the process screen platform. We can sort about this control chart alarms which gives us how many of the variables violate the test one. Test one is simply out of control limits. As you can see, the most alarms I get from the most stable process. T his is a bit paradox. As you can see here, I have a very stable process and the SPC limits that gets calculated automatically gives me very much false positives. ` I shouldn't react on this because it's just within the variation. The second problem with it is if I have an order and analyze the samples, it calculates the control limits for me. Like in this examples, it says it's all good. I f I later on analyze a second order, the calculation of the limits automatically switches to the new process variations. I t makes the window bigger and again says, hey, everything is okay until we see of course that it moved and the variation got bigger. W hat I did making an SPC script that deals with the measurement and loads and stores SPC limit in the database so we can have a proper working SPC. I would like to switch to demonstration of how this looks in our database. W hat we have is a script that imports the data. What does is it goes into the database and search the whole product order we have stored for this demo. I made a special data set and it automatically contains this program. For us is like article or part. W e can select like PO1 that the first one we did in our example, I can say okay, I want to have a look at that data and at the back and this is the final result. A s you can see here, after [inaudible 00:04:23] and SPC. In our example, this is what happened. I have a stable process, everything is good. Look at all processes. This is where the error came from. Again I can show you the SPC. Y ou see here, in the beginning the process was okay. Then there was a phase from where the variation got bigger and then it got back again. T his X1 got a problem. The problem was X2. As you can see here, I got some parts out of spec. This, I would like to change now. A s we discussed earlier, what we need to do is go to the first PO and set this table one I want to go back to and I make now run the script that goes over the table that is active and extract every line with spec limit. I see here, this is the spec limits and you can see here empty control limits. W hat I can do now is two ways. I can either activate this PC here, have a look at the data and say zoom in a bit and say manually on X2 lower control may need 2.98 and 3.02 and down. Tell the program hey, please update the limits for me and I go back to-- No this was wrong , sorry. 2.97 and 3.01, update. T he limits I just set are now in the control limits which I can check in X2 it added the control limits for myself. The problem is now if I close the table and read download the data, these control limits are lost. W hat I can do now is save it, close all, reload the data and the script now looks if there are control limits present for X2 and I did none for X1 and if I go here now SPC X2, the control limits are now set as desired. If I have a lot of control limits present in my data set, like 20 or so, it could be quite difficult to set the limit manually for everyone. This is where I made the script that you can actually select the desired variables you want set default. A s I showed you earlier, if I would set it to one, it would copy the automatic calculated limits from the system which can be too tight. T his case I say okay, I want the margin twice as big. Now it runs through every row and sets automatic the limit. I save the limits to the database and if I run the limits again, you can see it's centered and it has a nice window around them quite easily. I can manage limits for a lot of variables. If I go now to the problem we had. I now select all of them, run the script again and run it again. Now it takes over the limits I set earlier. A s you can see here, I would get warnings earlier on that something is wrong and then we could have prevented this from happening. A s you see here with some countermeasures and now we are back again into the stable phase. What I use in the script is I search with spec limits to identify which one are variables to work with. This can also solve problems with platforms who depend on spec limits. I can filter them and only make limits or control limits for SPC or spec limits for the process capability platform. I don't get an error message that control limits are missing. What is important is of course safety. Habit of jump is that the source is in the file. W hatever password or database connection you use gets stored into the table. This of course, can reveal server name or even username and password. To get around this there's a preference ODBC Hide Connection String. What you also can do is in the script itself, encrypt the code with password and username. You see in here, so people are unable to read it. When I write into the database, I use the code create database connection. I set the reference and then there is SQL statement I put together from insert into which database name it is. Then what I write that's from the list I generate program names, empathize and the control limits. Th en execute these SQL statements from the connection string and the SQL string. What's important is that when the log is longer so I get an error message from the SQL, it beeps me and put it in the log. W hen something is working wrong, I can check it like credentials are wrong or the connection could build up or whatever it's possible. Then I write the control limits into the data table. I check for the name of the article and if there are SPC limits present, if they are, I update the column properties with the control limits which then get automatically displayed in the SPC and the program handles accordingly. Second use for writing something back is if we have a measurement we make a test with it like environmental test or endurance test and make a second measurement. I have the same part with two measurements or I have a false measurement like something went wrong. I retake the measurements and the second measurement is actually what counts. T his I can navigate by a small script that says update DB. I can give me a dialogue if I want to set the measurements inactive or update it. If I want to update it, I can type which label I want and what's the name of it. It works similar as the function name selection in column. What it does is for each measurement I get a unique ID, even if the part itself has a serial number. I have the serial number measured twice after zero hour, after 100 hours from test option one. I t got different measurement IDs. This is how I differentiate between the measurements. For the inactive part, that's simply that in the SQL statement I can say if the measurement were inactive disregarded so these faulty measurements don't show up. K ey points is careful that you may not give out some sensitive information like password usernames. You can hide certain parts in the script with encryption the code is JSL Encrypted and then the encrypted code, is in here as a text. U se pref ODBC Hide Connection String. A nother way is use windows authentication to avoid credentials altogether. This also can help that you can use specific columns that are writable like the label one, label two column and to avoid manipulations. We have to policy a user can set a measurement inactive but he cannot delete it. I f we will search to the database, we can restore all the data if something went wrong. Another good practice is check that the data is actually written into the database like a handshake and enrich the data with important information that can be handy for user years later. Because they may don't have the information w hy you took tree measurement of the same part and maybe wondering why it's getting worse and in that all have the same information to work with. I can straight this little future view. I can go back in here for the demo. Usually when we have press builder make it row there in the string second two. What I do is select those parts row, name selection in column and that would give me a column which I then can save. I f I reload the data, this information is lost. What I can do now is this feature update database. I can update it, I can say, hey, label one, this is stable. Y ou can see here, label one is now marked as stable. I can go and say okay, these measurements update I want to leave on stable, close it and I simply put the data from the database and as you can see the stable, unstable faces are stored. I f someone later makes it, then they can bow conclusion from the phases here is missing. This is the stable face, this is un stable face and the same goes with data. I can select some object inactive. It will warn me if I want those trees inactive a nd the next time I would call the function they wouldn't show up. If I want to have them back, I can select here include arrow meshes that those measures come back again. T his suits my script. I will thank you for listening and if you have further questions I will be [inaudible 00:19:42] later on. Thank you very much.
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Automatization of Immunohistochemistry Data Analysis for Protocol Validation (2023-EU-30MP-1318)
Saturday, March 4, 2023
Cerba Research is a global company providing high-quality, specialized analytical and diagnostic solutions for clinical trials. Cerba Research Montpellier develops customized immunohistochemistry protocols to detect the expression of selected targets on patients’ tissue sections. To be used in clinical trials, these protocols must meet the regulatory agencies’ criteria to ensure that the protocol will allow consistent results on precious patients’ samples. With the diversity of parameters evaluated and the types of evaluation possible in implementing these custom protocols, automating data analysis became a need. Thanks to various JMP® tools, we have developed an automated analysis that saves time and homogenizes protocol performance reports by including statistical and graphical data in a Dashboard. This process, submitted as JMP Add-in, has been incorporated into our user workflows, thus facilitating our procedures. Used version: JMP v16.1.0 Hello. My name is Marie Gérus-Durand, and I'm working for C erba Research Montpellier. Today, I will show you how we set up automatization of immunohistochemistry data analysis for protocol validation in Montpellier using JMP and its tools like dashboard and add-in functions. First, some words about Cerba Research, it's a worldwide company with capabilities in all the continents. Here I highlighted in yellow the department I'm working for. It's a Histopathology EHC Department. As you can see, I'm based in Montpellier in France. W e have also other labs in US, in New York, and in Taiwan in Taipei. First of all, what is immunohisto chemistry? The aim of the technique is to detect targets protein mainly on a tissue sample. Here you have a slice of a tissue, for example, when you do a biopsy. We will look at the targets of interest using antibodies, which will detect the target. This antibody is recognized by another one which is combined with chromal fluoroform or active components that allow the detection of the target. Here you see, for example, these three components are highlighted, meaning that antibodies bind it and we can detect it. After the experiment, we can can look at the slides under microscope or using a scanner, which allow visualization and analysis of the results. On the next slide, I just zoom in so you can see better what it looks like. Here it's a skin sample and you have a cell nuclear in blue and the target of interest in red. One of the challenges within immunohisto chemistry and histopathology is that you have many possible protocols, colorations. Here on the left, it's two different histological colorations. It doesn't involve antibodies like I show you, but it just reactive with the different components of the tissues. You see here for the MOVAT, we have five colors. For the HE, we have only one color, but the intensity depends on the type of structure you are looking at in the tissue. On the right, you have two immunohistochemistry protocol. One simplex, we called it, because we detect only one target, and it's a chromogenic here. It's in brown. On the top right, you have a multiplex. H ere we detect many components. Here it's a fourplex, four targets on the same slide, and each is revealed by different flow of work. You have different color for each of the targets. Among of these coloration detection possibilities, then you have a multitude of possible analysis method. The slides can be analyzed by a pathologist, which will give us qualitative or semi- quantitative data, or by image analysis, which will give us quantitative data. Another layer of that is that you can have reportable parameters which are single. For example, if you have a simlplex, you detect the targets, only one target, and you assess only one parameter like percentage of positive cells, for example, or you can have many. For one target, you can have the percentage of positive cells and a specific histology score. Or if you have a multiplex, then you can multiply this for all targets in the multiplex. Each report level parameter is target- dependent. You can imagine that we have a lot of combination that we can have to access during our validations. In Cerba Research M ontpellier, in 2022, we have a small part, like 20 % of our project related to animals. We are studying animal samples. The other projects were on human samples. Among this, most of them are four clinical trials of the project. That's a very [inaudible 00:04:56] . W e have some others that are outside clinical trials, a quarter of them, and a small portion, 3 % that are CAP compliant. CAP is a specific regulation for US. It is to know that before being used in a clinical trial, we should demonstrate that our protocol that we developed in Montpellier show consistency in results for section of the same sample. If we analyze the sample at different type points, for example, the samples of patients involved in the study, the first year should be the same than in the five years after. On the different automatons, we have at our different sites, and when the samples are analyzed by different operators or pathologists. This applies a rigorous validation according to the health agency, and this validation is mainly based on statistical criterion. The implication for the company is that we need to increase the team members to support the increasing number of projects we have each year, and we need a normal genus statistical analysis pipeline to be sure that we will give all our clients the same type of results. Obviously, we need statistical analysis tool, and it's when we choose JMP to support our validation of the protocol. Today, I will show you only a part of what we are doing because I don't have time to show you everything. I chose a quite simple example. It's a kind of experiment we do to validate the precision of the protocol, meaning that we check the intra-run , which is called repeatability precision over three slides. Here are the three ones highlighted in purple in the same cycle. The three slides I run at the same time, they come from the same sample, and we just check that we have the same data. The inter-run, reproducibility, test over two slides highlighted in blue in each cycle. In total, we have six slides over three cycles. For reportable parameters, I will use an image analysis dataset, which is quantitative data and usually easier to analyze. We will have two reportable parameters for one target. How do you start? First, we need to import the data. I don't know if you are familiar with that. But in our case, we have data either from Word documents, so we use the Word Import Tool available on JMP community website . I put the link here so you can go and find it again. Or we import data from Excel either directly by opening the file in JMP or by using the JMP tool in Excel. For this presentation, just to be faster, I create some script to help me to focus on the dashboard creation after that, which will take more time. But this is just some script and just to be faster, but I will not develop into it then. Here I will open a data table from Project X here. As you can see, it's a quite simple table. I have four columns with the validation, the slide ID, which are internal slide numbers, and the dataset for my two reportable parameters. This data continues because they come from image analysis. Once I have this, I will need to prepare my data. It's a most time consuming part of analysis data. Again, it's why I have a script. You see the five columns where I did. The sample to be able to correlate each data to the same sample, which is a part of the slide ID we have internally. I just get a formula here to help me to do that. The slide number, which are the last digits of the slide ID, and the slide order, the 1, 2, 3, 4, 5, 6, 7 for each sample. Thanks to this slide ordering, I would say, I implement the repeater. The three first slides which were staying in the same cycle for repeater, and two first slides of each cycle for reproductivity test. Here I have all the information needed to do my analysis. I go back to my journal, and we will want to do the dashboard question. I still have some steps to do before that because I would like to have all the analysis I want to put in the dashboard. Here are the two little table you see where we are required to analyze the CV of our protocol for each sample for repeatability. I selected only the three first slides thanks to the local data filter. The same for reproducibility, where you see I have the slides from the reproducibility column. Here are the data that I need. This data I updated them in here. You see I have much more columns now. It's easier to find the name here. I have the sample CV for repeatability, for reportable parameter 1 and 2, and then the same for reproducibility for the two parameters, and then I do the mean of both samples for each of these columns. Here, all the data I will need to implement in my dashboard. I can cross this table. I don't need them anymore. I will now do the graphs and tables that will really fit in the dashboard. Again, what I want to show to the client is the distribution of the data for repeatability. I put as well the standard deviation and the mean. Usually, it's pretty good here and for reproducibility where I have all my six slides. Then I would put a table with a CV for each sample for repeatability and the reproducibility on the left for the first parameter and on the right for the second one. The same outline for the mean of the two samples. These are the four part I want to show on my dashboard. I will show you how it looks like. I want to obtain something like that. This is often I did that I can show it, the two graphs and the two table. It's what I would show you how to do now. You see that all the graphs are to the same data table, sorry, and it's much easier to do the dashboard after. I saved as well all the scripts so I can redo them whenever I want. I will create a new file, new dashboard. You have many templates. I usually start from blank and just you have to put in what you want to see. Sometimes it's a bit difficult because it's small, but we always manage to find our way. My table at the bottom, you just drop them where you want to have them. It's pretty simple. You can change the names of each part, so I will not do it just. But you can see that you can edit all the parts. You can run your script and then give you the dashboard. It's pretty similar to what I showed you before. I have my two tables at the bottom and my two graphs at the top here. I have inverted the two, so I usually prefer to start with repeat that. I will just switch them. If I put it where something was already, they just switch. Here we are. This is the layout I want, so it's good. After, you can play to see better, more or less, of the table, et cetera. But this is just for visual [inaudible 00:15:00] process. Then, okay, now we have dashboard, but it would be more interested if we can directly go for the dashboard when you have all your data you want on the dashboard and click and you have a dashboard. For that, we just need to do an add- in. It's quite simple. Thanks to the magic triangle, I call it, the red triangle here. You click Save Script and just To Add-I n. Then it will create a script that will do the same dashboard again. Let's go back to the data table. I will close this one. You are sure that the one that you will see is not this one. Sorry, I shouldn't have closed it before doing the dashboard. I will just use this to be faster and not to create it again. Here, if you click on the red, Save Script, To Add -In. Then this is the name you will have in the add- in list, but it's to manage your add- ins, I would say, but it's not the one that will figure out in the add-in tab in JMP. The name that you're in the add-in tab is this one. For today, I would just call it Test so I know which one is this. Save. You see here, you have all the script used by JMP to do this dashboard, and I will save it in our Project X. Here you see it take the name on the first tab Dashboard only. I save it. Here I have this both tick Install after save, so it was already put in my add-in list. If the box was not ticked, then you have just to go to the location. You save your file and click on it and it's installed. Now I can close this dashboard . I have created my complement already. Now, how to use it? I just went a bit faster. If you open it, it installs. As I have it already, I will not start it. It's just under it. If you go to View, Add-ins, sorry, Dashboard, and Unregister, it's not erased. I will find it again when I go to my project. Here you see I have it. If I double -click, it ask me if I want to install it. Sure, I want to install it. It's back here again. You can share it. You just have to copy the same file I clicked on and paste it in a shared folder or send it to a JMP user colleague. You can modify it. For this you have to go in Open. Again, this is the dashboard but then the black click this time, just go on the arrow here to open and Open using Add-In Builder. Here you go back to the first time window where you have your script here, and you can either edit the script or put other functions that I don't really use, to be honest. But you have many functions. I'm sure you will find more information on JMP website about that. This, for example, will allow you to put all the preparation step in the same complement. When you run, everything is done at the same time. This is it. In conclusion, using this dashboard and add-in functions allow us to have reports consistency because we have always the same set up of results to send to the clients. We increase the traceability. Thanks as well for the use of the scripts because we are sure that we are all doing the same. I t's a great time saver because as you say, I just have to click on one button and I have my dashboard. If you combine this with a precision timing. All the data preparation, then you have your table, you click on one button, and you have everything done. It's a great time saver. It's all I wanted to show you today. I hope you enjoy it. If you have any question, don't hesitate to reach out to me either by email— you have the email on the first slide here— or through the JMP community. Thank you.
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JMP® in Qualification and Validation of Biological Assays (2023-EU-30MP-1283)
Saturday, March 4, 2023
Statistical evaluation for biological assays is critical because a lot of data needs to be summarized for reporting to customers and authorities for drug registration. JMP® is a helpful tool not only for calculating the required parameters but also for automating evaluation. For example, it can be used to graph and automate calculations for Repeatability, Intermediate Precision, Linearity, and Robustness of a Relative Potency Assay. Because these calculations are often required, a single JMP file for calculating the parameters saves a lot of time and can be used by novice users. Furthermore, the evaluation remains consistent throughout various assays, even when different technologies are used, such as SPR, ELISA, or Cell-Based Assays. Hello, and welcome to my presentation about JMP in qualification and validation of biological assays. I've divided this presentation into five parts. At first, I want to give you a small introduction about my person and the company I'm working for, VelaLabs. The second part is a general introduction about method qualification and method validation like we perform it at VelaLabs often. The third part is how we collect and summarize the data. Then I will continue with the JMP data table where I've created some scripts to evaluate the data generated during qualification and validation. The last part, I will talk about some additional robustness parameters where different functions of JMP are used. My name is Alexander Gill, and I'm at VelaLabs since 2019. I'm a laboratory expert in the l igand binding assay group . I'm mostly responsible for method development, qualification, and validation for Biacore assays and ELI assays. VelaLabs is a contract laboratory for quality control u nder GMP conditions. We have four operational departments: the ligand binding assay group, the physico- chemical group, and the cell- based assay group, and the microbiological group. Method qualification and validation is important in the life cycle of pharmaceuticals and biologicals. Here, the life cycle of such drugs is shown from the pre- clinical phase over the clinical phases and the application. During the pre- clinical phase, developed methods are suitable which are on the scientifically sound. Afterwards, for the clinical trials phase 1 and phase 2, we use mostly qualified methods. For method qualification, we show with some suitable parameters the performance of the assay. If the assay is then validated, derived from the data generated during qualification, we create limits which must be reached during method validation. The validated method afterwards is used for clinical trials phase 3, new drug application, and also for batch release in post-marketing afterwards. Here, I've shown some examples for the performance parameters. The accuracy shows if the method has any bias or shift, or especially it lacks bias or shift/ the intermediate precision is the variability between runs where we show that different operators and different devices on different days do not influence the result. The repeatability is the variability within one run where we try to keep the differences between the reported values as small as possible. The linearity shows the dose response of the assay over the whole assay range. During the robustness, we show that different parameters can or cannot influence the result. For example, different ligand lots or different models of devices. Then the sensitivity to detect stability- indicating changes, there we use mostly stress samples to show that they can be easily distinguished to non- stress samples. Specificity is, for example, a blank subtraction or positive or negative controls. The data collection is mostly performed in Microsoft Excel because it's more accessible within our company. I will come later to this. We also collect the reported value, which is the final outcome of the assay. The reported value is calculated using a validated software like PLA, SoftMax Pro, or the Biacore Software. This is to ensure the data integrity. Every step where a human is involved in the evaluation has to be checked by a second operator. As I use a relative potency assay as example for this presentation, I've also shown here what's the reported value for this assay. It's the relative potency with the 95 % confidence interval as a quality parameter. Here are the reasons why we use Microsoft Excel for the data collection because it's available on every PC within our company and every employee has basic knowledge about it. The raw data from the validated softwares are also often exported in Excel. What is really important that the data in Excel are organized in datasets, so they can be transferred to JMP more easily. Here is a basic experimental design for a method qualification or validation. The first six runs are basically designed around the intermediate precision where we use 50%, 100%, and 200 % sample mimics in each of these six runs. These runs are spread above two devices, two operators, and performed on three different days. We report the mean relative potency for each of these dosage points, the standard deviation, the coefficient of variation, and the 95 % confidence interval. For accuracy, we use the same dataset as for intermediate precision, but we calculate the mean recovery, and therefore standard deviation, C V, and 95 % confidence interval both for all 18 datasets together and also for each dosage point separate. The seventh run is for the determination of repeatability where we use six 100 % sample mimics within one run and also report the mean relative potency, standard deviations, CV, and the 95 % confidence interval. Then for linearity, which is here in run 1, we use the sample mimics for intermediate precision and additionally use 75 % and 150 % sample mimic within this one run to show that the results are linear over the whole assay range. Therefore, we report the correlation coefficient, the slope, Y-intercept, and residual sum of squares. For robustness, in this case, we show a lower and a higher immobilization level and also use two different lots of the ligand. Then now, I'll show you the Excel table where we can see here in the first few columns the metadata for each data set, then the reported value with the 95 % confidence interval, the slope ratio, which is additional quality parameter and shows afterwards if the analyte is comparable to the reference. The column for recovery is empty because the recovery will be calculated in the JMP software. Here, the matrix where it's defined which datasets are used for which parameters. Then there are two different possibilities to transfer this data into the JMP software. One is with this function where a data table can directly be created out of this table. But in this case, I won't use this function because I have already created a JMP table with all the scripts I need. I just copy all the data. But for this procedure, it's important to show all available digits of the reported values because only the shown digits are pasted afterwards into the JMP software. I now copy with CTRL+ C all this data and then go to the JMP data table where I can paste all this data. Then we get here an alert because in the column Recovery, I created a formula to calculate the recovery. I don't want to paste the data in here, but the Excel table does not contain data in this column. We click Okay, and everything is pasted as we wanted. For what purposes JMP can be used under GMP conditions? We use it during the method development phase for design of experiments, for example, to investigate more different parameters of the method within one set of experiments. Then use it for the statistical data analysis and also for comparability studies. For example , if a customer wants to compare a biosimilar with the originator. During qualification and validation, JMP can also be used for the design of experiments. For example, for the intermediate precision parameters or to spread the robustness parameters over the qualification runs. Then I will show afterwards for the determination of assay performance in qualification and for the check of the assay performance during validation. But for this, an additional QC check is required afterwards if all the calculations are performed in the right way. This is very important that JMP is not really usable for the determination of reported values. Therefore, as I mentioned before, we used mostly validated softwares. Now we go to the JMP data table where I will first show you how I create most of the script. Therefore, I use distribution. For example, if I create the accuracy at 50 %, I select the Recovery and choose it for the Y columns. Then I click Okay. Then we have here all available datasets. To limit these datas ets, I create a local data filter and use Accuracy and edit. If it then choose all the columns indicated with an X, we have reduced the data sets to 18. To reduce it further for only the 50 % sample mimics, I add with the AND function an additional filter for the nominal potency, which I then limit to the sample mimics with about 50 % nominal potency. Then you see we have only six datasets left with mean recovery of 99 % and a coefficient of variation of about 6 % and the confidence interval. To save this script, I go again to the red triangle here and save the script to data table. For example, as accuracy 50 % 2, because I've already created a similar script here. The difference for the intermediate position, if we open, for example, here the intermediate position at 100 % is only that we not use the recovery here, but the relative potency and have also again the same parameters reported. For repeatability, w e choose only one run with the six 100 % sample mimics. We report also the same data like the mean relative potency, the standard deviation, the 95 % confidence interval, and also the coefficient of variation. What's also very interesting here is the linearity where we use a different function. I created this using a Y by X plot and plotted the relative potency by the nominal potency and created a linear fit through all these data points. Then we report the Y-intercept, the slope of the linear fit, the RS quare or coefficient of correlation, and also the sum of squares error or residual sum of squares. Then we go back to the presentation. For additional robustness parameters, we, for example, show the performance of the assay using different material lots. For them, we show if they have equal variances. If the variances are equal, we use the T-t est. If not, we use the Welch- test. For example, for ELISA methods, w e also measure sometimes the plates on two different models of plate readers to show if both models can be used. This is then analyzed using a paired T- test. At the end, I want to thank you for your attention. If you have any further questions, you can type it into the Q&A or contact me directly.
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Advanced Statistical Modeling
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Data Exploration and Visualization
Mass Customization
Predictive Modeling and Machine Learning
Quality and Process Engineering
Reliability Analysis
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Data Driven Selection as a First Step for a Fast and Future Proof Process Development (2023-EU-30MP-1260)
Saturday, March 4, 2023
This talk will focus on how JMP® helped drastically reduce the cultivation experimentation workload and improved response from four up to 30-fold, depending on the target. This was accomplished by screening potential media components, generally the first, and sometimes tedious, step in fermentation optimizations. Taking characteristic properties such as the chemical composition of complex components like yeast extracts enables flexibility in the case of future changes. We describe the procedure for reducing the workload using FT-MIR spectral data based on a DSD setup of 27 media components. After several standard chemometric manipulations, enabled by different Add-ins in JMP® 16, the workload for cultivation experiments could be drastically reduced. In the end, important targets could be improved up to approximately 30-fold as a starting point for subsequent process optimizations. As JMP® 17 was released in the fall of 2022, the elaborate procedure in version 16 will be compared to the integrated features. It might give space for more inspiration – for developers and users alike. Hello everyone, nice to meet you. I'm Egon Gross from Symrise, Germany. From professional, I'm a biotechnologist and I'm looking forward for my presentation for you. Hello everyone. I'm Bill Worley. I am a JMP systems engineer working out of the chemical territory in the central region of the US. Hi. My name is Peter Hersh. I'm part of the Global Technical Enablement Team, working for JMP out of Denver, Colorado. Peter welcome to our presentation, Data- driven selection as a First Step for a Fast and Future -Proof Process Development . First I want to introduce the company I'm working for. We are located in Holzminden, more or less in the center of Germany, and there are two sites coming from our history. Globally seen, we are located with the headquarters in Holzminden . We have big subsidiaries in Peterborough, in Sao Paulo and in Singapore, and there are quite a lot of facilities in France also coming due to our history. Coming to the history, Symrise was created in 2003 out of a merger from Harmon and Rhymer, which was founded in 1874, in Dragoko, which is the other side, from our facility, which was established 1990. Over the years there have been quite some acquisitions and also in 2014 the acquisition of Diana, which is mainly located in France because that's the reason why there are so many different research and development hubs. Our main products come from agricultural products or from chemical processes and there are quite a lot of diverse production capacities, production possibilities for our main customers being human or pet. As is so diverse, we are dealing for food for the human consumption, for pets consumption, and also for health benefits. On the other side, the segment Scent and Care is dealing with fragrances coming from fine fragrances, to household care, to laundry , whatever thing you can imagine, that smell nicely. As I said in the beginning, I'm a biotechnologist by training and I'm dealing a lot with fermentation processes to optimize them and to scale them up or down. One major issue when it comes to fermentation is the broth, the liquid composition of the media, which will then feed the organisms. No matter which organisms that are, they need carbon sources, they need nitrogen sources, they need minor salts, major salts, pH values, and other things. It is often important which kind of media one has. When it comes to media composition, there are two big branches which can be seen. One is the branch of synthetic media, so all components are known in the exact amount and composition. The other way are complex media, for example, having a yeast extract or a protein extract or whatever, where it's a complex mixture of different substrates, different chemical substances. The third approach would be a mixture of both. One of the side effects of these complex media is that it's quite easy to deal with them. But on the other hand, there can be constitutional changes over time, as some vendors tends to optimize their processes, their products, to whatever region, to whatever target. Some customers get hold of those changes, some don't. Another issue might be the availability o f it's a natural product like [inaudible 00:04:38] or whatever. You might know some ingredients, you will surely not know all ingredients and there might be promoting or inhibiting substances within those mixtures. At the beginning of a process development, the media is of main importance. Therefore I tried to look at carbon sources, nitrogen sources, salts, trace elements, and so on, being my different raw materials. While growing the organisms, one has to take care of different temperature, stirring velocities to get oxygen into the liquids, cultivation time, and there are a lot of unknown variables to get an idea what the effect might be to the cell dry weight, for example, or to the different targets compounds one has in mind. For this setup, I used then the definitive screening design. As the most of you know, they are quite balanced and have a particular shape which is reflected in these three- dimensional plot. You can see definitive screening design is somehow walking around certain edges and having a center point. Due to the construction of the definitive screening design, one can estimate interactions and square effects. These interactions are not confounded with the main factors and the main factors itself are also not confounded with each other. This is a very nice feature of the definitive screening design and therefore they are very efficient when it comes to the workload compared to formerly known screening designs. Some disadvantages are also there. One big disadvantage is if you have about 50% of the factors that are working that have a high influence or even more, you have a significant influence, significant confounding, which you have to take care of. In this particular case, although it's the leanest possible design I found, the practical workload would require five to six months just for screening. This is far too long when it comes to a master thesis. The alternative was then to build another design or to build another process. I was so inspired in Copenhagen 2019 by a contribution from Bill where he talked about infrared spectroscopy and I thought why that might be a good idea, using the chemical information hidden in a near-infrared spectrum to describe the chemical composition of the mixtures. Therefore I established this workflow. First, the media preparation was done of all the 65 mixtures. Then the near- infrared spectrum was measured, some chemometric treatments were preferred and afterwards, the space of variation could be held constant at a maximum, but the number of the experiments could be reduced quite significantly. To show you how the workflow is, I started, as I said, with spectral information. One of the first principles one has to do is to make a standardization of the spectra to avoid baseline shifts and things like that. This is one way to make it. Introducing a new formula to standardize, or what I did, I used an add-in to preprocess and calculate the standard normal variety, which is when it comes to the digits, the same as the standardization, as we see here. With this standardized spectra, depending on each measurement, I continued then and compiled first all these spectra. What you see here on the top is the absorption of every sample. We have an aqueous system so we took water as a reference. A fter building the difference between the absorption and the water, we then got deeper and saw differences within the spectra. One of the big question was do I calculate first the difference between the absorption of the sample and the water and calculate then the standard normal variety? Or do I first calculate the standardization and then use these standardized values from the water background? One could think the procedure is the same, but the outcome is different. As you see here, on the right- hand side of the dashboard, I zoomed into this area and in the lower part, the curves have a different shape, a different distance from each other than in the upper part. T his might have then an influence on the subsequent regression analysis. Therefore, I selected first to make the standardizing and then the difference calculations. After I did these first steps, then came the chemometric part, that is smoothing and the filtering and to calculate the derivatives. This is a standard procedure using an add-in which is available. You can imagine that the signal might have some noise. This is seen here in the lower part, the red area is the real data, and the blue curves are the smooth data. On the left upper side, you see the first derivative. On the right upper side, the second derivative of these functions. If it comes to polynomial fits, it's depending on the procedure, what you are fitting, what's the polynomial order, and how broad your area is, where you make the calculations in. If we take here only a second- order polynomial, you see that it might change. Now, this is not a two, this ought to be a 20. Then the curve smooths out. Although it's smooth, you can see differences in height, in shape. To get hold of those data, one has to save the smooth data to data tables, separate data tables. Then I tried different settings for the smoothing process, because I did not know from the beginning which process is the best to fit my desired outcome of the experiment at the end. After quite a lot of smoothing tables, which were then manually done, and I then concatenated the tables. These are all the tables we just made. I'm going to the first one and say, please concatenate all of the others. The nice thing is that you then have at the end, these different distances coming from the smoothing effect. I had a second polynomial order. A third polynomial order is 20 points to the left and to the right for the smoothing process and 30 and so on. This is just a small example to show you the procedure. I did quite more. What I did was this amount of treatment. I had [inaudible 00:15:01] for a second, third, or fifth polynomial order with 10, 20, or 30. Now came the big part to decide which particular procedure represents my space at best. This, therefore, I made a principal component analysis of all my treatments I did. This is a general overview. The numbers represent each experiment by its own that you can follow them in the different scores and loading spots. The loading plot is… That's a regular picture of a loading plot when it comes to spectral data. If you take into account that we are coming from a design, this value of 24% explained variation at the beginning for the first component is very high. Why? Because the factors of the definitive screening designs are orthogonal to each other and independent from each other. One would expect lower values for the principal components. After this treatment, the first derivative with second order polynomial and 10 points to the left and to the right for the smoothing, it looks very evenly distributed. You might think of a cluster here on top or below. I went through all of these particular processes and selected then a favored one, where I saw that the first principal component has a very slow describing power for the variation. That's then the way I proceeded. After selecting the particular pre- processed data, I still have my 65 samples. But as we heard at the beginning, 65 is far too much for a workload. If you ask yourself, why is there 132 samples? That is because I copy pasted the design below, the original design for the spectral treatment I used then. If you want then to select your runs you are able to make due to time reasons or due to cost reasons or whatever, this is one process you can make use the coverage and the principal components. Then this is the design space which is available dealing for the all variation which is inside. But as you see, we would need to make 132 experiments. If we then go just select all the principal components and say please make only the one which are possible, then you have the ability to type in every number you want to. At this stage, I selected several smaller or bigger designs and saw how far can I go down to reach at least a good description power. I made these 25 experiments, let JMP select them. The nice thing is with this procedure, if you are coming back to your data table, they are selected. But this procedure I didn't do right at the beginning. At the beginning, I made a manual selection. How did I do that? I took the score plot of the particular treatment and then selected manually the outer points as good as possible. Not only in the picture of the first and second principal component, but I went deeper. This, for example, is the comparison of a selection method I just showed you with the DOE of the constraint factors and with the manual selection, just for showing you maybe some differences. If you make this DOE selection several times, don't be confused to get not always the same numbers, the same experiments, which might be important. With this approach, I then reduced the workload from 64 experiments to 25 experiments. In all of these experiments, all my raw materials I had from the beginning were inside. I didn't leave any raw material out, and that was very nice to see, that I could retain the space of the variation. After the cultivation in two blocks, which took a frame week of three weeks for each block, we yet then analyzed our metabolome and the supernatant and determined our cell drive mass. For time's sake, I show you only the results and the procedure for the cell dry mass. Other molecules might be the same procedure to be done then. The next issue I had was that there is a confounding. I had to expect the confounding because I had only 25 experiments for 27 mixtures coming out of a design where I knew where I supposed to have interactions and quadratic effects. These interactions is nothing new when it comes to media composition. Quadratic effects were nice to be seen. Then came the next nice thing, which was introduced by Pete Hersh and Phil K. It's the SVEM process, the Self-V alidated Ensemble Model. In this sketch, you see the workflow and we will go through that in JMP. The first thing was to look at the distribution of my target value. After making a log transformation, I then saw that it's normally distributed. So we have a log- normal distribution. That's nice to know. The first thing was to download this add- in, Auto validation Set-up, and hit the run button. We then get a new table. The new table has 50 rows instead of 25 rows from our original table. Why is that so? The reason for that is while hitting the button, the data table gets copy- pasted below and we get a differentiation into the validation set and into the training set, as you see here. The nice feature of this Auto validation table is that you can, due to a simulation, find out which parameters, which factors have an influence. This happens by the spared fractionally weighted bootstrap weight. If you look for example, the second experiment has a value of 1.8 in the training set and the same sample has a value of 0.17 in the validation set. This then gives one the ability to have a bigger weight for some samples in the training set and vice versa in the validation set. While they have a bigger value, a bigger weight in the training set, they have a lower weight in the validation set. To analyze this, it's necessary to have the pro version to make a generalized regression. As we took the log value of our cell dry weight, I can then make a normal distribution and then it's recommended to make a lasso regression. From the first lasso regression, we get a table for the estimates, and now comes the nice part. We make simulations changing the paired weight bootstrap weight of each factor. For time's sake, I'm just making 50 simulations. From these 50 simulations, we get then the proportion for each factor we had in the design where it entered the regression equation, or didn't enter the regression equation. This pattern comes due to this randomization process of the bootstrap forest method. From this distribution we go to the summary statistics, customize them, we are just only interested in the proportion nonzero. This proportion nonzero is finally the amount of the 50 simulations. How often this particular variable went into the regression equation. From this, we make a combined data table and have a look on the percentage of each variable being in a model or being not in a model. This looks a little bit confusing. If we are ordering it by the column two descending, we then see a nice pattern. Now you can imagine why I introduced at the beginning this null factor or these random uniform factors. T he uniform factors were manually introduced. The null factor was introduced by hitting the auto- validation set. What do these points mean? These points mean that until the null factor, these variables have a high potential because they were quite often within the model- building processes. These at the bottom were quite seldom within the model- building processes so the ability to reduce your complexity is given by just discarding these. Here in the middle one has to decide what to do. After having this extraction, not losing information, and not losing variation, one can then think of different regression processes making response surface model or step wise regression or whatever regression you have in mind. It's wise to compare different regression models looking what's feasible, what's meaningful. That was the procedure I used in JMP 16. While coming now to Pete and Bill, they will describe you something else. Thank you, Egon. That was a great example of an overview of your workflow. Thank you. What's new in JMP 17 that might have helped Egon a little bit with the tools he was working with? I'm going to start off with a little bit of a slide show here. I'm going to be talking about Functional Data Explorer. That's in JMP Pro and talking about the pre- processing and Wavelet modeling that are built into Functional Data Explorer now. All right, so let me slide this up a little bit so you can see. What's new in JMP 17? We've added some tools that allow for a better chemometric analysis of spectral data. Really any multivariate data that you might have that you can think of, these tools are there to help. First is adding the preprocessing methods that are built into FDE now. We've got standard normal variant, which Egon showed you. We've got multiplicative scatter correction, which is a little bit more powerful than the standard normal variant. Both of these will not disrupt the character of your spectra. That's not the story with Savitzky-Golay. It does alter the spectra, which will then make a little bit harder to interpret the data. The key thing is it still helps. Then we have something called polynomial baseline correction, which is another added tool if you need that. The next step would be then to save that preprocess data for further analysis, like principal component analysis, partially squares, so on and so forth, so you can do some analysis there. The Wavelet modeling is a way to look at the chemometric data similar to principal component analysis. We're fitting a model to the data to determine which is the best overall fit for, in this case, 25 spectra. That's the goal here. It's an alternative to spline models. It's typically better than spline models, but not always. You get to model the whole spectra, not the point- by- point, which you would do with other analysis types. Then you get to discern these things called shape functions that make up the curve. These shape functions are, again, similar to principal component analysis in that they are helping with dimension reduction. Then, as I said before, these are excellent tools for spectral and chromatographic data, but virtually any multivariate data is fair game. These are just an example of the Wavelet functions that are built in. I could try and pronounce some of these names, but I'll mess them up, but know that these are in there. There is a site here that you can look up what these Wavelets are all about. I got the slide from Ryan Parker so thank you, Ryan. Almost last but not least, what we're doing with this functional principal component analysis is we're trying to determine, again, what's the best overall fit for these data and then compare the curves as needed. What comes out of the Wavelet modeling is a Wavelet DOE, and we determine which wavelengths have the highest energy for any given spectra or whatever we're looking at. These Wavelet coefficients can then be used to build a classification or quantification model. That's up to you. It depends on the data and what supplemental variables you have built in. In this case, this is a different example where I was looking at percent active based on some near IR spectra. Let's get into a quick example. All right. This is Egon's data. I've taken the data that was in the original table, this absorption minus the water spectra, and I've transposed that into a new data table where I've run Functional Data Explorer. I'm just going to open up the analysis here. It does take a little bit to run, but this is the example that wanted to show. We've done the pre- processing beforehand. We've taken the multiplicative scatter in this case and then the standard normal variate, and then built the model off of that. After this function or these pre- processing tools which are found over here, I'm going to say that data out, and then that data is going to be used for further analysis as needed. To build on the story here, we've got the analysis done. We built the Wavelet model. After we've gotten the mean function and the standard deviation for all of our models, we build that Wavelet model and we get the fit that you see here. What this is telling us is that the Haar Wavelet is the best overall based on the lowest Bayesian Information Criteria score . Now we can come down here and look at the overall Wavelet functions, the shape functions, and get an idea of which Wavelets have the highest energy, which shape functions are explaining the most variation that you're seeing between curves, and then you can also reduce the model or increase the model with your selection here with the number of components that you select. One thing that comes out of this is a Score Plot which allows you to see groupings of different in this case, spectra. One that you're going to see down here is this. This could be a potential outlier. It's different than the rest. If you hover over the data point, you can actually see that spectra. You can pin it to the graph, pull that out, and then let's say let's just pick another blue one here and we'll see if we can see where the differences are. It looks like it might be at the beginning . If we look at this right here, that's a big difference, then maybe that just didn't get subtracted out or pre- processed the same way in the two spectra. I don't have an example of the Wavelet DOE for this set up, but just know that it's there. If you're interested in this —this has been a fairly quick overview— but if you're interested in this, please contact us, and we will find a way to help you better understand what's going on with Wavelet DOE and preprocessing built into JMP Pro. Pete, I will turn it over to you. All right. Well, thanks, Bill and Egon. Just like Bill, I am going to go over how Self-Validating Ensemble Models changed in JMP 17. Bill showed how you could do what Egon did in 16 in 17 much easier using Functional Data Explorer. For me, I'm going to take that last bit that Egon showed and with the add- in, creating that SVEM set up. Using those partially weighted bootstrap columns and then also making that validation and the null factor. I'm going to just show how that's done now in JMP® 17. So this is much easier to do in JMP 17. Just like that, spectral data processing with FDE, this is done in JMP 17. If you remember, Egon had gone through, he looked at all those spectra, he extracted out the meaningful area, looking at smoothers, the standard normal variant, and did a bunch of different pre-processing steps. Then he took those preprocessing steps and he selected a subset of those runs to actually run, and he had come up with 25. Here is those 25 runs. From this step, what he did is that Self-Validating Ensemble Model or SVEM. In 16, this took a little bit of doing. You had to make that model, then you had to simulate, then you had to take those simulations, and run a distribution on each one of them, and then get the summary statistics, and then extract that out to a combined data table, and then graph that or tabulate that and see which ones happen the most often. That was a lot of steps and a lot of clicks to do, and Egon has clearly done this a bunch of times because he did it pretty quickly and smoothly, but it took a little bit of doing to learn. Clay Barker made this much easier in JMP 17. Same 25 samples here, and instead of running that Auto validation Set- up add- in that Egon showed, we're going to just go ahead and go to Analyze and Fit Model. We'll set up our model. I f you remember, we're taking this log of the dry weight here. We're going to add a couple of random variables along with all of our factors into the model, and then we're going to make sure that we've selected generalized regression. This is the set up for our model, we're going to go ahead and run it, and in JMP 17, we have two new estimation methods. These are both Self-Validating Ensemble Model methods. The first one is a forward selection. I'm going to go ahead and use SVEM Lasso because that's what Egon used in his portion, and here you just put in how many samples you want. He had selected 50. I'm going to just go with the default of 200. Hit go, and you can see now it's running all of that behind the scenes where you would have simulated, recalculated those proportional weights, and then at the end here, we just have this nice table that shows us what is entering our model most often up here. Then when we hit something like a random variable. Just out of randomness, something that's entering that model is entering maybe about half the time. Things that are entering more regularly than a random variable, we have pretty high confidence that those are probably variables we want to look at. Then we would go from here and launch the Profiler. I've already done that over here, so we don't have to wait for it to launch or assess variable importance. But here, this shows us which of these factors are active. We can see the most active factors, and while it's not making a super accurate model, because again if you remember, we are taking 25 runs to try to estimate 27 different factors. If you take a look here at the most prevalent ones, this can at least give you an idea of the impact of each one of these factors. All right, so that basically sums up what Egon had done. It just makes this much easier in JMP 17, and we are continuing to improve these things and hope that this workflow gets easier with each release of JMP. Thank you for your attention and hopefully, you found this talk informative.
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Advanced Statistical Modeling
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Consumer and Market Research
Data Blending and Cleanup
Data Exploration and Visualization
Design of Experiments
Predictive Modeling and Machine Learning
Quality and Process Engineering
Reliability Analysis
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Advanced Workflow Builder: Using the Reference Manager To Create Custom Tools (2023-EU-30MP-1339)
Saturday, March 4, 2023
The new Workflow Builder introduced in JMP 17 is a great time saver for automating a fixed set of tasks. But wouldn’t it be great to create a workflow you could use on any data table? With Workflow Builder’s Reference Manager, you can! In this presentation, we will show how to record a workflow using a specific data table, then modify it so it can be used on any other table. Using this technique, you can build a custom tool with minimal JSL coding and share it with your colleagues. Hello, my name is Michael Hecht, and I am here to talk today about the workflow builder, which is a new feature in JMP 17 that allows you to record operations that you do within JMP and then play them back to recreate those operations. If you attended the plenary session this morning, then you saw Mandy Chamber's demo of workflow builder, which gave a good overview. Mandy also gave a talk this morning that goes into more detail on the workflow builder user interface. I'm going to start talking about a more advanced feature of the workflow builder called the reference manager. This is a feature that allows you to take a workflow and manage how references to data tables and columns within those tables are mapped and resolved. It allows you to make workflows that are more generic to be used with any data table. Let's get started. I'm going to start by creating a new workflow which is under File, New and there's New Workflow. You see the little tag that says this is a new item for version 17. When I choose this, I get this untitled workflow builder window. The panel in the center lists the steps of my workflow and as you can see, they're empty. I'm just going to click the record button, which is this button with the big red dot. You see it changes appearance to show that recording is in progress. Then I will do some operations in JMP and have them be recorded. First, I'll go I'm going to go to the File menu and I'm going to open up a data table. We see Big Class here opened and if we look in the workflow builder, we see that a step was added to do that same operation. Now I'm going to go to the Analyze menu and I'll do a Fit Y by X to do a one- way analysis using age as my X factor. For the response, I'm going to create a transform column by right clicking and choosing formula. I'm going to use weight divided by the height squared, which is the formula for body mass index. If we were using metric units of kilograms over centimeter squared, then that would be sufficient. But Big Class has its measurements in imperial units of inches and pounds. We have to multiply this by a scaling factor to get a standardized BMI. Now I have my formula correctly, I'm going to rename my transform column 'cause I don't want to use the default name, I'm going to call it BMI. Click okay. Click okay. Now BMI is in my list of columns that I can use to create the report. I'm going to add that as my response. Click okay. Here's my one- way analysis of variants. You notice, though, that it did not yet add this step to the workflow builder, but there is a little note at the top saying, hey, I see you launched a platform. A s soon as you finish with the analysis and close the window, I'll add it to the workflow. That's so the workflow builder can capture any changes you might make to the analysis. For example, I'm going to turn on means ANOVA here to get the means diamonds. Now when I close the window, you see the step gets added, report snapshot, Big Class, Fit Y by X of BMI by age. Great. I am done recording. I'm going to click the button again to stop recording. Then I'm going to turn, switch to presentation mode for this workflow. Like the tool tip says, that removes some of the editing controls. Namely it removes the record button so that I don't accidentally hit it again, and it takes away this activity log at the bottom. I'm going to rewind my workflow to the beginning, which closes windows that were associated with it. Then I'm going to click the Run button to replay it just to make sure it does what I wanted to do. I click Run and it opens Big Class. Then here's the analysis just like that we had it before. That's great. I want to look at these different steps in the workflow. You can see that behind each one is some JSL that shows up in the tool tips. But I can open up this step settings panel to see more details. I'll click on the first step and we see that there's some metadata information and then the JSL to run it, which is just an open command and there's a path name to the file. When I click on the second one, I see that we have JSL that sends a one- way message to this reference to the bigc lass. Jmp data table. Inside the one- way, we see references to weight and height and age. There's a reference to BMI, but BMI is computed as a transform column right there. That all looks good. But when I run this workflow, I don't always want to run it on Big Class. I might want to run it on a different data table. I'd like the user of the workflow to be able to choose that data table. I really don't need this open data table step at all. I'm going to select it and click the trash can icon here to remove it from the workflow. Well, now what's going to happen when I run my workflow? Let's give it a try. I'll click the Run button and it immediately prompts me to choose a data table. The data table Big Class, it can't find it anywhere. It's not in the list of tables that are currently open in JMP. It's asking me, do I want to go find it and open it or maybe open a different table? It says that anywhere that Big Class is referenced, it will use the table that I opened. It has a list here, but there's only one item in it . I'm going to select that and click okay. Now it's prompting me to go ahead and open a table. Well, I have Big Class right here, so I'm going to drag that in and click Open, and it runs just like before. Well, that's great. Let me rewind and run it again, but this time I want to choose a data table that's not Big Class. Let's see what happens. I'll click okay, and I want to use this table Football. Football is data about a college football team playing American football as opposed to rest of the world football. I'm going to open that and there it is. But you see, I'm immediately prompted, hey, this data table does not have a column named age. What would you like to use instead? I'm going to choose position and I'll click okay, and I get my one- way analysis. Notice that it did not prompt me for height and weight because those columns already exist in the data table, so it just uses them directly. T hese positions are now the categories that it's using for the one- way analysis, and they're all the abbreviations for different positions in American football. You have your defensive back, defensive lineman, full back, half back. I don't know what IB is, but kicker, offensive back, offensive lineman, quarterback, tight end. You see the wide receivers have a nice little group here with low BMI because they have to be fast. That's all cool. But I noticed in this football data table right below position, there's a second variable called position 2. I f we look at what that is, it's a different categorization of the data. Position divides the data into 11 categories, but position 2 divides it into 7. It might be interesting to run my workflow using position 2 for comparison. I'm going to rewind this and I'm going to run it again, but this time I'll choose position 2. Well, wait a minute, it didn't even give me a chance to choose the variable. It just went ahead and used position. In fact, it didn't ask me what data table to use. It decided Football was already open, so it could just use that. Somehow the workflow is remembering my choices from the previous run. If I want to choose a different variable, I have to somehow prevent that from happening. Let's take a look at this workflow. We see that there's an option on the red triangle menu that says References and has a sub menu, allow replacement of references. Well, that's already checked and the tool tip says it allows prompting for tables and column references that it can't find. Well, that's exactly what happened the first time we ran it. But then when we ran it again, it reused the replacements that already had. But here's the second option, Clear Replacements. The tool tip here says that it clears the previous replacement choices, which is what we want. Let's do that, and then we'll rewind this and run it again. Okay. Now it's prompting me for a data table again. Because Football is one of the tables already opened, it appears in the list here. I can just pick it, click okay, and now it's prompting me for age again. This is great. I can pick position 2 and click okay. You can see the seven categories that are defined by position two here. Okay, well, let's go back to position 1 now. Rewind this and run it again. Again, it didn't give me a chance to choose position 2. I don't really want to have to clear the replace ments every time that I run this workflow. What I need is some way to control which replace ments are remembered. I see this third option here that says Manage. P ool tip says it manages the table and column references that can be replaced at runtime. Let's see what we can do here. This brings up a window called the Reference Manager. At the top is this check box, allow replacement of references, which is pretty much the same as that first sub menu choice. Just like that, when it's checked. Then there's a button reset all replacement choices, which sounds the same as the clear replacement menu item. Then we have a list of table references. There's only one item in the list. That's because my workflow only has the one reference to Big Class. If this were a more complicated workflow that accessed multiple tables, then all the table references it used would appear in this list. You select one of them and then you have details that you can change. I can see that I can add a custom prompt rather than the big long prompt. I'm going to use something a little simpler. How about please choose a data table. I see that this mode is set to prompt if necessary. It's necessary when it can't find Big Class or it can't find what you see Big Class is mapped to here Football. But what I want is for it to prompt every time I run the workflow, so I want to choose each run. Then down here, we have a list of the column references that the workflow uses from this data table. We see BMI, which is the transform column. That should never be prompted before because we're computing it within our workflow, so I can change this one to never. You see age is remembering its mapping to position and it prompts if necessary. We want to change that to prompt on each run. I'm going to also give a custom prompt for it about to please select a category column. I'm going to copy that so I can reuse it. Height and weight are both referenced here and as we saw, it can pick those up automatically if they exist in the data table. That's good. I think if necessary, it's doing what we want. Let's leave it at that, but I'll give it a better prompt. We'll change this one. The same thing for weight, we'll change here. I think we have all of our settings the way we like it, but we still want to clear these mappings to the Football data table. I'm going to click button and hopefully, those will go away. All right, t hat's good. I'll click okay. Let's rewind this and try it again. It's letting me choose, and I can go back to position. You saw the new prompts in both of those dialogs, the custom prompts that I chose. That all works well. In fact, I can even... Let's see, let's go open our Big C lass table. If I run the workflow now, you'll notice that even though the workflow was originally recorded using Big Class, because I set the prompt to each run, it's prompting me now instead of just using Big Class directly. But I'm going to choose Big Class. Now, even though Big Class has an age column, it's asking me to select a category so I can choose something different like sex. Then you get one way of BMI by sex. This is all doing what we want. I am going to save this workflow from the File menu, Save, I'll give it a name of BMI and it automatically gets an extension of . jmpflow, J-M-P F-L-O-W. We'll save it to the desktop and there it is. You see it has this little org chart looking icon. If I get info on it from the finder, we can see that the hidden extension .jmpflow is right there. This is great. I could distribute this to my colleagues and they would open it on their installation of JMP. Then when they run it, it will prompt them for a data table, it'll prompt them for a category column, and it'll produce the report. That's fine. That's a great way to do it. I think I'm going to take it one step further, though. I would like to package this within an add- in. An add- in lets you customize items on the JMP toolbar and menu bar. For example, you can see I have an add- ins menu here already with some items in it, but I'm going to create a new add- in to put my own command there for BMI. Let's go to the File menu and we'll create a new add- in and this add -in builder dialog appears. Let's give it a name. We'll call it BMI Report. Add-ins need a unique identifier, which is just a string, but we use this reverse DN S system that you take your company's website, like ours is Jmp.com and reverse it, s o we'll use com.j mp. I am the only hashed at JMP, so I'll put that in here, and I'm going to give it something unique for this specific ad- in that I'm creating. We'll call it BMI-R eport. I'm going to select all that and copy it. This is version 1 of my add- in. As I said, the workflows are a new feature of JMP 17, so I'd like to set the minimum JMP version to 17. Unfortunately, it looks like we forgot to add that as a possibility to this menu. I'll just do the best I can and choose 16, and hopefully, we will get that corrected for JMP 17.1 coming next month. I want to add a menu item to the add- ins menu. I'll click add command and we'll name it BMI Report. I'll even give it a tool tip, create a one- way analysis of body mass index by the chosen category. That's pretty good. Now I do need to add some JSL here, but it's pretty simple. All I want to do is open bmi.j mp flow. However, I'd like to embed my BMI workflow within the add- in that I'm creating, which means I need to tell the open command that it comes from this add- in's home directory, which I can get to with the path variable dollar add- in_ home. I have to give it the add- in's unique ID as well in parentheses and put a slash there for the directory separator. That looks good. Now I need to actually add my workflow as a file embedded in my add- in. I'll go to additional files and add it there. That I believe is everything. Let's close this, save changes. It gives me a default name of BMI Report. We'll save it on my desktop. There it is. It should have this workflow embedded in it. I'm going to close it here and I'm going to put this workflow that I built in the trash, empty the trash. When we save an add- in, JMP automatically installs it as well. If I come over to JMP and look at my add- ins menu, now we have BMI Report. When I choose that, it should open the workflow that's embedded within the add- in. There it is. We can run it. We get our custom prompts before, we can choose a table, we can choose a category. I am going to redo the analysis here so that I have a copy of this that's not under the control of the workflow and will stay around even when I rewind the workflow. Let's rewind it. I'll run it again and let's choose position two. You can compare the two reports and let's see, we can look at things like, this O category under position two that corresponds to the full back, the half back and the quarterbacks, so it's the offense. L is the defensive line and the offensive line, so that's the linesman. Anyway, that pretty much concludes the items I want to cover in this talk. I direct you again to Mandy's presentation this morning, Navigating Your Data Workflow: Workflow Builder Grants Your Wishes for Data Cleanup, for a great overview of of the rest of the workflow builder UI. I believe at this time we are going to take live Q&A. Thank you very much.
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Advanced Statistical Modeling
Automation and Scripting
Basic Data Analysis and Modeling
Consumer and Market Research
Content Organization
Data Access
Data Blending and Cleanup
Data Exploration and Visualization
Design of Experiments
Mass Customization
Predictive Modeling and Machine Learning
Quality and Process Engineering
Reliability Analysis
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Using Graph Builder’s Splines To Align Measurement Curves (2023-EU-30MP-1329)
Saturday, March 4, 2023
A frequent task in data analysis is aligning curves before a descriptive or root cause analysis. Often an additional complication occurs when the measurement intervals are not equidistant in the series to be compared. There is not one single value that quantifies the shift for a whole curve. Interpolation is the solution in cases like this. Simple linear interpolation may lead to numerous random errors; a spline interpolation is more robust. Since the Graph Builder exports the formula for the spline in its current shape, it became an easy, accessible tool for the alignment of curves. And as all steps can be programmed in JSL, it provides a framework for automating curve alignment. This presentation will describe the background, concept, and case study application for the alignment of curves. Welcome, everybody, to this presentation about a use case of curve alignment. Experienced analysts often say that in a larger analytical project, plus minus 60 % of the total time goes into the preparation of data. If curves play a role and especially the alignment of curve is needed, then that is certainly close to the truth. Curves are very specific types of data, and JMP has some tools to work with curves and to address all the related problems. In the sample library, there is the Algae Mitscherlich data, which is one of my favorite data sets with that respect because it has the option to deal with many aspects of fitting curves. This is just an example of the development of Algae density in different treatments. The type of curves that I'm going to talk about are typically observations or measurements over time. But this doesn't mean any loss in generality. The presented concepts work in all kinds of curve relationships. This is an example, A lgae measurement over time, and one of the aspects that is in the focus of the analysis for this data set is to specify curves, specific types of curves that have a known shape and are driven by certain parameters and then to estimate those parameters based on the data. In those cases, the parameters very often have a technical meaning like slope, inflection point, limit that gets approached. That platform here also has the sliders that let you analyze how changing one of those parameters affects the shape of the curve. In the specific case that we are going to talk about, we are not specifically interested in the curve. The curve itself is only a help because we are facing another problem. This is the data set, or this is a part of the data set that goes back to the real problem. We had this series of measurements, one and another series of measurements, and they belong to two different devices. U nfortunately, the clocks of these devices were not in sync. But luckily, each of the devices had one sensor that measured the same substance. W e could look for times where the measurements were very close to each other. Then try to find out how to correct one of the clocks, so to say, so that we get aligned measurements, and then use those to evaluate the data from all the centers that have been available in that data set. What was the problem of the task? You see the curves here. The red curve is the one that we took as the reference curve, and the blue one is the one that we wanted to shift. You see not only that the curves are quite some distance away, although they should theoretically have measured the same substance at the same time, but also the time points of each series is completely, or the time point of both series are completely unrelated. With the bare eye, we don't see any lag that we could use to correct one of the data sets. Therefore, I looked into… I compared the time points, not the Y measurements, the time points of the two series. If there was just a shift, then we would expect to see all the data, all the points exactly on one line. But here you see there are ups and downs, so this is obviously not the case. Perhaps we can see more, we can understand more if we calculate row by row in the data table, we calculate the difference of the two times and look at those. Here with some fantasy, we see a little bit of curvature, so till the end, it seems to be closer related than to the beginning. But in the beginning, this looks like real random data. This as well does not help us a lot to figure out how to relate the data. I thought I had the link to the data table, but we can look in this screenshot as well. This is the data set that you have seen before, a little bit annotated. We see that two lines have specific markers, the star and the circle. This is due to the fact that the whole measurement project had a ramp- up phase, and at the star point, the measurement series, the measurement time, the real process time started. The circle is there where we, after visual or manual inspection, saw the starting point in the second time series, and we want to align both. W e need to change the relationship of the rows, of the data and the rows. We want to shift one of the data sets, and that reminded me of the paternoster that I like to use when many years ago I was working for a company that had a very old administrative building and we had the paternoster in there. It came to me that the strategy that we are following is part of paternoster shift, which gives the word elevator pitch a completely new meaning, by the way. How do we find the right steps, the right place to fit? We do not have similar or identical time points in both series of times. We need to construct those time points somehow. Of course, this is done through interpolation. T he first thing that comes into mind is linear interpolation, and if I zoom in into only a part of the data set, then it is evident that linear interpolation, so just checking, so to say, the regression between neighboring time points has some problems, especially if we look into areas where we have horizontal lines, which may easily happen. Then the time point in that range is quite arbitrary. It always leads to the same results. The opposite is true. If we are in an area with a very steep ascent, then a little change on the X- axis or the time may lead to significant changes in the Y value. T his is not a very good technique. We can use splines to interpolate between the values. You know splines, certainly from the graph builder. If you make a scatter plot, then by default, the smoother is switched on and the smoother provides splines. You can even change the stiffness or the degree of fit or the closeness to the data with a slider in the graph builder. The advantage of using a spline as an interpolation tool, also takes into consideration points further away. T hey build a smooth curve. That is why in the graph builder, it's called the smoother. This makes it easier to use those for interpolation and to use these as well as a base in our alignment process. We need to fit splines. How can we do this or which platforms do help? First of all, simple tool is Fit Y by X comes into mind very fast when you work with JMP and do that data analysis. This is the data, one of the curves. There is the spline fit, and here is the slider that let me choose how close or how close I want to fit my data. Very good, very easy to use, and you can save the spline but only the values, not the formulas. We are keen on getting the formula for the spline. Next stop, Fit Model. If you have a continuous variable, you select it, you can give this the attribute of being a knotted spline effect. When you do so, you are prompted to say how many knots that spline should have, the more, the more flexible. I accept the default, say run. We get the typical report from Fit Model. A lso, we have fit models, functions of saving formulas. We can use Fit Model, save the formula. Little disadvantage here is I need to specify the number of knots before I start the analysis. Once the analysis is done within the platform, I don't have the option to play with it or change it like it is, for example, in Fit Y by X. Another tool is the Functional Data Explorer . T he Functional Data Explorer has splines as a core function, and it is also functionality to find optimal definitions, optimal fits for the splines. You can export everything. It's a bit because simple tasks like this is not where the Functional Data Explorer is made for. You need some more clicks to come to a result. A lso, it's only available for people who have JMP role. Remains, the Graph Builder. You have seen it before, and this time I want to show the spline control as well. As I said, we can use the slider to determine the fit. A very nice feature, by the way, is that you can check this box, then through a bootstrap sampling method, the confidence interval for the smoother is calculated or estimated. You see how that changes when I'm… Now you can see better, I have a lot of data and there is not too much variability. H ere the confidence band is quite small. But if we zoom into one of these areas here, for example, that place and look at what happens when I change this, then we see that the smoother can even… That the line of the smoother can even walk out of its own confidence band. This is another visual help to find out a good fit for the smoother, for the spline. It should stay within its own confidence limit. Then comes the very important option here. We can save the formula. Then we have a formula for this spline. The graph builder surprises as a modeling tool. Who had expected this? How does that help? This is again part of my data table, small part. You see that now I have two columns here where I saved the formulas for the smoother too. Down here in the colored rows, I put some arbitrary time points in. That leads to an interpolated response relative to the time point that I have given. It only works for interpolation. We cannot extrapolate this way, it's only with interpolation. But this way I can, for example, manually add different time points. I have this one here plus X seconds in that case , and then I can see what is the difference of the interpolated value. Now I can put reference times in and I see exactly what is the expected value, plus minus a little bit for both measurements. I did this for two different phases. I can go here and experiment anymore. In my journal, you see in the yellow rows, I added eight seconds. In the orange ones, 10 seconds. Depending on what you want to do, this is the principle of how you can work with this. If your task is a one- off task, this is good enough. You can go in here, play with the data, see the difference. Our task was more regular. The good thing is everything can be controlled with JSL. As usual, for many commands that you do manually on line, you have corresponding JSL statement, and I just listed some. This is not a working program. First of all, you need to set up the graph, clear. Then you have commands that you can send to the graph and specifically to the smoother element in your graph. We can change the smoother so we could even interactively try to determine good fits. We can also give the command to save the formula in the data table. That is the command that plays an important role for our solution here. You can read out the current settings of the Lambda slider and something small. How did we want to use this? The concept here was, of course, first you need to determine what is the reference curve and what is the objective curve, the one that needs to be shifted. Then you calculate the spline function for the reference curve and determine the direction of shift. Where are we? Do we need to shift our time up or down? Then we move the Y values of the objective curve, one row in the desired direction and calculate the spline function for this new curve. Save that, use a reference value, and then calculate the differences in Y for each row. Then we take the total sum of those differences as a criterion when to break the process. Because after every step, we calculate that difference, we save the difference, we do the next step, and we check, was there an improvement? If yes, we move up or down one row more, and then we repeat that whole activity until there is no improvement anymore. The whole program in the real project, of course, runs behind the scenes. You wouldn't see anything. But I added did some graphs to make it visual to demonstrate how that works step by step. The starting situation is this one. On the left- hand graph, you see the dashed line and the solid line. The dashed line is the reference line. The solid line needs to be moved. On the right side, you see the differences per row. In the beginning, the differences are… You see that here in the starting area, the differences here are pretty small. Then they get larger and larger, and they are negative. That is why it goes down here on a negative scale, very small differences in the beginning, and then they get up larger. This is the starting situation. You will see this picture again. Then the program will start shifting the reference curve one cell up in our situation, our case. Then you see how these graphs update for every step. Yes, first we need to tell JMP what are the time and measurement values for the reference and the objective curve. Here we go. It will take a little bit in the beginning, then afterwards the steps come faster. You see how for every step, the blue curve approaches the dotted curve, and how the differences decrease. The last step did not improve the situation anymore, therefore, the program stepped one step back. Now, we have the data table in a situation where we shifted up the objective curve. Now we can use this shift for all the other measurements, for all the other sensor results that we had for this device and start the analysis. That was it. I hope I could inspire you a little bit. It was an interesting presentation. If you have any questions, please don't hesitate to contact me. My email was on top of the presentation, bernd.heinen@stabero.c om. Thank you very much.
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Want To Know What Analysis You Need? Just Ask the Data Analysis Director (DAD) (2023-EU-30MP-1204)
Saturday, March 4, 2023
When analyzing data, scientists and engineers often know what they want to accomplish but are unsure which statistical test is needed. This talk introduces a new Add-in, the Data Analysis Director (DAD). This Add-in was designed to make it easier to find the proper statistical test in JMP® based on the analysis task, goal, and type of data you have. DAD provides a guided flow to help you find the right analysis and run it in JMP. It includes built-in examples, links to JMP Help, demo videos, and even lets you launch the analysis on your data. As you will see, DAD is a useful tool that can help guide you along your analytic journey. Thank you for the introduction. My name is Mia Stephens, and I am a JMP product manager. And I'm also the lead developer of STIPS, which is our free online course that we'll talk about in a few moments. And my name is Peter Hersh. I'm part of the JMP Global Technical Enablement team, and I did a lot of work finishing up and developing the Data Analysis Director, which we're going to be covering today. I'm going to get us started. We'll start by talking about STIPS —Statistical Thinking for Industrial Problem Solving —and how the development of STIPS was really the beginning of the Data Analysis Director or DAD. If you're familiar with STIPS, this is our free online course. If you were at the Discovery in Frankfurt a few years ago, you heard us talk about this for the first time. STIPS is 30 -35 hours of online training for anyone who wants to learn how to build a foundation in statistical thinking. It covers the basics, from learning how to define a problem; exploratory tools, and how to communicate the message in your data, how to prepare your data for analysis; quality methods, SPC capability, measurement systems analysis; basic inferential statistics like hypothesis testing and sample size; correlation and regression; fundamentals in design of experiments; predictive modeling, and text mining. This is just an introduction of these topics. All in all, it's about 30 -35 hours. As we set out to develop this course, we wanted to make sure that we included the right topics and topics that are most commonly used in the industry. And we also wanted to make sure that we understood the challenges that users face in industry. Before we started developing any content, we did a survey. One of the questions we asked was, what are the most common analysis tasks and methods that you use in industry? SPC was at the top of the list with some of the other quality methods, DOE and hypothesis testing. This part of the survey allowed us to identify the general groupings of topics that we would include in STIPS. And relevant to this talk, the second question, what are the biggest challenges you face when you're using data to make decisions? We weren't very surprised to see data preparation at the top of this list, but something that was a little bit surprising was understanding which method to use and how to use it. As we're developing STIPS, there are a lot of topics included in STIPS, and if you're learning statistics for the very first time, we knew that this could be a little bit overwhelming. We developed this concept of a tool that would help you understand, "Well, which method do I want to use based on what it is that I want to know, what it is I want to do with data, and what type of data that I have?" A t the time, we call this the Data Analysis Assistant. It was basically an unfolding utility where it started with just a general statement. In general, what is it that you want to do? And then based on how you answer this question, it allowed you to drill down. If I chose, "I want to describe a group or groups," and then the next question I answered, "I want to explore relationships between two variables and my data is continuous," then it gave a recommendation. A statistical technique that might make sense is scatter plots. You can find this in the Graph Builder or in Fit Y -by -X. And we provided a link to some data sets that were used in STIPS. Our original plan was that we would have this really as part of STIPS to accompany STIPS so that people could refer back to it after the fact. But STIPS took several thousand man-hours to develop and time got away from us. Fortunately, Peter was on the STIPS development team and he saw the value of a utility like this. I'm going to turn it over to Pete and Pete's going to talk about how this original concept, this data analysis assistant, ultimately became DAD. Thanks, Mia. Let me share my screen here. There we go. The motivation from this actually really came from a couple of customers reaching out and asking exactly what Mia found in that survey. When they had new users coming to them, they weren't quite sure where to go into JMP to do the analysis they were after, so they didn't know what technique to use when. Several of our customers were communicating through their training organizations that, "Hey, it'd be great if there was some way to direct people to the analysis they wanted." I reached out to Mia, and she had already laid the groundwork with that data analysis assistant and developed all of the tasks that most people were needing to navigate to, and all I did was take that and finish it off. Let's get in and actually look at what this Data Analysis Director looks like. When you launch it, it's going to look like this, and this is just an application that is inside of JMP, and we have it deployed as an add-in. And we'll share the link on where you can get that add-in. But you'll notice here that as I pick a task from this side, it will give me several different options for goals for that specified task. And then when I pick a goal, it will let me know if there's different types of data that might have that same goal. Once I do that, then all of these buttons down here become active and I can do different things. So to give you an idea here, let's say I wanted to compare groups. I have two or more independent populations, and then there's only one type of data that I'm looking for. If I then launch an example, you'll see JMP will automatically launch this sample data set and run that example. T his is a great start and this is where we started with JMP 16 was the ability to do that. We can also take you right to the help menu for that specific test, the launch analysis, which will allow you to just bring up that analysis, and then also a demo video. This demo video just links to our learning library, which is this great resource that is basically answering a question of how do I do X task in JMP? In this case, the Data Analysis Director just allowed me to figure out that what I wanted to do was a two -sample T -test, and here's a quick 2- 5 minute video on how to do that. The new thing with JMP 17 that we added was this workflow. This is really nice for people who maybe don't have a ton of statistical background and maybe don't know what they're looking for inside a JMP. When I open the workflow... This is a new feature in 17. So if you're operating in JMP 16 or older, you won't have workflows. But with JMP 17, this is a nice new feature. When I hit play, what JMP does is it opens that data set, just like the example I had launched before, but now it has some extra capability in there. It's highlighting some of the reports where I should look. It's telling me about that report. It's also stepping through and telling me what each one of these reports means. Then at the end, I've done that. That's great. You could probably get there with scripting as well to be able to recreate this, but workflow makes this a lot easier. And then it also allows for a generalizable aspect to this. This is key, especially with this Data Analysis Director. We encourage folks to go ahead download this add-in, but you can make it your own. And how you might make it your own is by taking a look at the things that come with it. First off, with workflows, if I do not prompt JMP to open a data set, so I'm going to just remove this data set, and I hit play... I forgot to close that behind the scenes. Excuse me one second. I f this data set isn't open and I am looking for a specific analysis, so here I'll just open a different data set. If I hit play here, JMP is going to tell me, "Hey, I don't have that data set you are looking for, but unlike a script, I'm not going to crash. I'm just going to go ahead and prompt you to pick a data set." I'll hit okay, and then it says, "Hey, pick a continuous variable in that data set." Again, unlike a script, if it doesn't find that column that you're prompting it to find, it just won't run. For this workflow, it's saying, "Oh, okay, pick a continuous Y. All right, now pick a column to replace gender." And now it's running through that same analysis, it's giving me that same report. T his makes this very generalizable. T hat's the nice thing here with that workflow. If you want to make this your own, with the add-in, you get this nice easy table that has is all of the scripts behind the scenes that you can edit. You can use your own sample data set that is maybe more relevant to your company. You can use different workflows. We have our demo videos, but maybe you have demo videos that you'd like to use instead. This is very easy to tweak. This will be installed right with the add-in. Without having to go in and script things, the add -in is just looking for a certain row in this data set, so very easy to change that. One thing you might be asking, and maybe you've heard of this, is with JMP 17, we also got a new feature called Search JMP. You might be asking, "Well, when would I use DAD instead of Search JMP? What is the difference?" We did a nice job here of laying out the main differences. Search JMP is built right into JMP, and I'll show you what this looks like here in a minute, whereas the Data Analysis Director or DAD is installed as an add-in, so you won't have Dad by default, you'll have to go and install it. And the Data Analysis Director is really directed for new users, maybe people who aren't as familiar with JMP or aren't as familiar with statistics in general, where Search JMP can be used by anybody. You just need to know what you're looking for. So maybe if you don't know the technique, the Data Analysis Director is a better place to go. But if you happen to know the name of the analysis you want to do, Search JMP is an easier way to find that. You also get those example videos, example methods, and those workflows inside of JMP. For Search JMP, this is not example -based. This will launch the analysis for you, but it will not walk you through an example. It is also more comprehensive, the Search JMP. Data Analysis Director, we picked some of the things JMP can do and highlighted that. Search JMP will look through everything inside of JMP, including the help, the sample data, the scripting index, all of that. If I am inside of JMP here, any window open under Help, it's the second thing on the Help menu, and it's Search JMP. For folks who haven't seen this before, if I start typing in something like T-test, Search JMP will automatically open this up, and I can go to Topic Help, I can go to Go, I can launch this. You can see it's a lot like that launch analysis inside of the Data Analysis Director. Again, the difference is I just need to know the name of the technique I'm looking for. That's the difference between these two tools and when I might use DAD versus Search JMP. To summarize what we've talked about here, really, the whole point and motivation of the Data Analysis Director is to help new users determine which tool to use when. This all dates back to that survey Mia was using to figure out what people need the most help with, and that was a surprise result of that survey that came out of the S TIPS development. And then we want to help new users navigate JMP, so get over that initial hurdle of coming from either a different statistical tool or just not being as familiar with statistics. And really, I think Mia put this great when she said we really want to just help democratize statistics, help scientists and engineers who maybe haven't taken many stats classes be able to find what analysis they need more easily. And we do this with examples, applications of different methods. And like I showed, you can customize DAD to make it your own. So put in your own examples. If there's something very relevant to you and your company, put it in there. You can tweak the workflows, you can adjust the examples, the example data sets, all of that's really straightforward. And when we compare that to Search JMP, Search JMP is a great tool to find what you're looking for when you know the name of the test you're looking for. We will post this in the JMP user community. This is a free add-in. Here's the link to that add-in. You can also just search Data Analysis Director JMP in Google, and it'll be your top result. A lso for anyone who hasn't taken STIPS, we strongly encourage you to do it. It's a free online course, really gets to the core of how to use statistics in general, not just in JMP. It will walk through the examples in JMP, but it's a great course for folks who are familiar with JMP or not. A couple of people we'd like to thank. Julian Parris helped a lot on the front end with this. And then Don McCormack was really instrumental in us finishing off the add-in. He developed a lot of that application and interface you see there. We also had many other people who have tested and provided feedback. And of course, Evan for the lead developer of Search JMP, and he's right here at the conference. So if you have questions, please stop by the developer booth and talk to him. Thank you for your time. Hopefully, you found this useful and you will go and check out our Data Analysis Director inside the community and provide any feedback for any future development we might want to do on this. Thank you. And Mia, any last thoughts? No. Great job. Thank you. Thanks.
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Advanced Statistical Modeling
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Quality and Process Engineering
Reliability Analysis
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Pythonless Python Integration for JMP® (2023-EU-30MP-1265)
Saturday, March 4, 2023
Challenges with a JMP® and Python integration resulted in a search for an alternative solution that would allow for the evaluation and testing of the various Python libraries and powerful algorithms with JMP. This would enable JMP users to work with Python from a familiar JMP environment. After a few different iterations, a RestAPI service was developed, and when JMP calls this service, it dynamically creates a user interface based on the options the service currently provides. The JMP user can then utilize this user interface to employ different algorithms such as HDBSCAN, OPTICS, and UMAP by sending data directly from JMP in one click. After the algorithm has finished its operations on the server side, it will return data to JMP for further analysis and visualization. Welcome to the Pythonless Python Integration for JMP presented by Murata Finland. My name is Philip O'Leary. Shortly about Murata, we are a global leader in the design, manufacture, and supply of advanced electronic materials, leading- edge electronic components, and multifunctional high- density modules. Murata innovations can be found in a wide range of applications through mobile phones to home appliances, as well as from automotive applications to energy management systems and health care devices. We are a global company, and as of March 2022, there was approximately 77.5 thousand employees worldwide, just under 1,000 in Finland, where we are located. Our product line up here in Finland include accelerometers, inclinometers, gyroscopes, and acceleration and pressure sensors. Our main markets are the automotive, industrial, healthcare, and medical. Today, we have two presenters, myself, Philip O'Leary and my colleague, Jarmo Hirvonen . I've been working in the ASIC and MEMS industry for over 40 years, 32 of which have been here at Marata. I've had several roles here and have come to appreciate the importance of data within manufacturing. Most recent years have been devoted to supporting the organization take benefit from the vast amount of data found from within manufacturing. I currently lead Murata's data integration team. Jarmo, perhaps you'd like to give a few words on your background. Yes, sure. Hi, I'm Jarmo Hirvonen and I work in Philips' team as a data integration and data science specialist. I have been using JMP for four and a half years, approximately the same time that I have been working at Murata. I'm a self- learned programmer. I have been studying both programming besides a couple of basic courses at university. In my position, I do a lot of JSL scripting. I write adding scripts, reports, automatisation, basically almost everything you can script with JSL. If it stays mostly inside JMP. I'm active JMP community member. I'm also a super user there. B ecause due to my background with the JSL scripting, I'm also steering committee member in the community scripters club. I have also written, I think at the moment nine add- ins that have been published to JMP community. Feel free and try them out if you are interested in that. Thank you. Thank you, Jarmo. This is the outline for the presentation that we have for you today. As this session has been recorded, I will not read through the outline as you can do so yourselves afterwards. Why do you have the need for a JMP Python integration? Well, basically, we are very happy with the performance and the usage we have of JMP. It doesn't require any programming for the basic usage, and we see this as a big advantage. JMP's visualization and interactive capabilities are excellent. T he majority of people performing analysis at Murata in Finland are already using JMP. W e have a large group of people throughout the organization using JMP, and we want to maintain that. However, on the Python side, we see that Python has powerful algorithms that are not yet available in JMP. We already have people working with Python in various different applications, and we have models within Python. We want to support these people and also help others understand and take advantage of the Python world. B asically, we want to take advantage of the wide use of JMP here at MFI and offer JMP users access to some common Python capabilities without the need for themselves to program. I'll continue here. Share. JMP already has Python Integration, but why we are not using that? Basically, there are two groups of reasons, JMP and us or our team. My experience regarding JMP are from JMP 15 in this case. JMP update at least once broke this integration and it caused quite a few issues for us because we couldn't use the Python JMP scripts anymore unless we modified them quite heavily. Getting JMP to recognize different Python installations and libraries has been quite difficult, especially if you are trying to work on multiple different installations or computers. Also, JMP didn't, at least that then support virtual environments that are basically necessary for us. Then our team side, we don't have full control of Python versions that JMP users are using or the libraries and packages they are using. Because not everyone is using JMP as the main tool. They might be using Python and they have some versions that don't work with JMP and we don't want to mess with those installations. Also, in some cases, we might be running Python or library versions with JMP doesn't support yet, or maybe it doesn't support old versions anymore. What is our current solution for this Python JMP or JMP Python Integration? We are basically hosting Python server using a web framework. We can create endpoints to that server, which are basically, behind them, there are different algorithms. We communicate with Rest API between JMP and the server. This is the biggest benefit. This test we can use JMP with the server, but we also have a couple of additional benefits. We can have centralized computing power for intensive models. For example, we don't have to rely on the laptop to perform some heavy model calculations. The server is not just limited to JMP. We can also call the endpoints from Python or for example, R. We are not dependent on the JMP supported Python and library versions anymore. We can basically use whatever we want to. Next, I will go a little bit away from the PowerPoint to jump and show a little bit of the user interface. First, I will explain some terminology which might appear here and there on this presentation. W e have endpoints, basically, this path here is endpoints. These come directly from the server. Then we have methods. It's the last part of the endpoint, DSME and XT boost in these two here. Then we have parameters, this column, and this is basically the inputs that we will send to the server. Then we have what I call stack or we call stack. It's the collection of stack items. O ne row is the stack item that we can send one after another to the server. Quickly jump here. W hat features we have? We have easy to add new endpoints. Basically, we write at the end point to the Python server, we built the server, we ran the JMP add- in, and this list will get updated. This adding support dynamic data table list. I f I change the table here, it will update here. Also, if new table is opened, the other screen, but it doesn't really matter. You can see it here, the untitled3 tree was opened. Then we can send data directly from here to the server, but they're pressing multiple different options for sending. I can send these selections that I had here basically immediately. I will show the results here. After getting the data back, we join it. These are from the server. We join the data to the original data table we had and then we have some metadata we can get from the server between the from the communication. Notes, column, properties telling what method and parameters were used to get these two columns. Then we group them. I f I have run multiple models or methods, it's easier to see which are from which runs. Then we have table scripts which are also grouped. This is different screen, let's move them around. We have stack. What was sent? HTTP response from the sent that comes from the server. Then in this case, we also receive from the endpoint an image. In this case, it's a scatter plot from the t-SNE components. I said already earlier, we can send multiple items from the stack one after each other. You can build, let's say, HPP scan with different input parameters used in the, let's say, 20 here and then 20 to 40, add them to stack and just send them there and come back when they're done and you can start comparing if there are some difference between those. T hen endpoints have instructions how to use them. Documentation link, if we have one short description, in this case, very short description of the endpoint, and then what each of the parameters do. Minimum values, maximum values, default values, and descriptions of those. Then we also have user management. In this case, I'm logged in as a super user, so I can see these two here experimental endpoints that basic user would not be able to even see. Then back to PowerPoint. This may be a partial implementation, partially how the adding works. When the user runs the adding, the JMP will ping the server, and if the server is up and running, JMP will send new request for the JSON that we will use to build the interface. The JSON is passed and then the interface is built and it is using JMP- type classes that I will show a bit later. C ustom class is created in JMP. A t this point, users can start using the user interface. User fills the selections, parameters, data tables, and such, and then sends the item from the stack. We will get the columns based on the inputs, get the date that we need and convert that data to JSON. In this case, I call it column JSON because there's a demonstration. Basically, normal JSON would always have the column name duplicated. Each row will have all the column names here. I n this case, we will have column name only once and then list of values. This makes the object we send much smaller. Before we send the data, we will ping the server again. This is done because we have different timeouts for ping and the request. Otherwise, JMP will lock down for a long time if the server is not running and we are using two minutes timeout, for example. T hen when the server gets the data, it will run the analysis, return the analysis results, and we join them back table at the metadata table scripts and so on. A t this point, users can start to continue using JMP, send more items from the stack, or maybe even JMP to graph builder and start analyzing the data that he or she gets back from the server. T his is the JMP- type classes. W e have different classes for different type of data we get from the server. We have booleans. I n JMP, this is checkbox columns, enumerators, this would be combo box type number, TypeS tring, and not implement that. This is basically used to check that the server is correctly configurated. This is a quick demonstration of one of those Type Column. On server side, it has been configured like this. When we request the JSON, it will look more like this. Then this type column class will convert it into an object that will look in the user interface like this. From here you can see that for example, minimum items is one. It's the same as minimum here. Max items, same thing. Then modelling types have also been defined here. We can limit minimum, maximum values, and so on based on the schema we receive from the server. A ll of these are made by the custom JMP classes. T his is enumerator, some options, then number boxes, and here is the boolean. N ow, Phil, we'll continue with the couple of demonstrations of the Pueb interface. Thanks, J armo. All demonstration is done today will be performed using standard JMP 16. There are three demonstrations I'd like to go through, each having a different task in mind. The first one, I'll just open the data set. This is a data set which contains probe or test data from five different products. It's a rather small data table just to ensure that we don't get caught for time. W e have 29 probe parameters for five products within the same product family. T he task at hand is to try to determine quickly, do we have anomalies or do we have opportunities for improvement. Looking simultaneously at these five different products, 29 different parameters, such that we could identify something that could help reduce risk or something that perhaps could reduce cost and improve yield. O ne possible way to do this, of course, would be the one factor at a time whereby we would just manually march through all the different data, all the different parameters and look for patterns. Very inefficient for 29 parameters, it's okay, but some of our products have thousands of parameters, so it's not the best way to approach the task at hand. Another possibility would be to take all of these parameters and to put them through some clustering algorithm to see, could we find groups naturally from the data that we have? I want to use the JMP- PyAPI interface that we have here. Jarmo already explained briefly how these work, but I will demonstrate it. T he intention that I have now is to make a HDBSCAN . I'm going to make the scan on all the probe parameters. I'm going to use the default settings. Default settings are typically already quite good. And I'm going to send this... I'm not going to make a big stack. I'm going to send this setting straight for analysis. W e can see rather quickly, the algorithm came back and suggested that I have a cluster. There are actually three clusters and one grouping of wafers which do not, in fact, belong to any of the clusters. Knowing that I have five products, I'm going to go with this for the sake of demonstration. I can see from here a histogram of the number of wafers in each cluster, but it doesn't really give me a good visualization of what's going on. I'm going to also do a dimension- reduction procedure. I f I go back into the same interface, and now I'm going to do a teeth knee dimension reduction on the same parameters and send it immediately. Wait for the dimension reduction algorithm to do its job, and it will return back two components for teeth knee, one and two, against which then I can actually visualize the clusters that the HDBSCAN gave me such that if I now plot teacher 1, teacher 2, and colour code them in accordance with the clusters that have already been identified. As I said, we have three clusters and one grouping of wafers which don't necessarily belong to a cluster. Maybe somewhat disappointing knowing that I have five different products. T hankfully, I have an indicator of the product. It's here. I said, this is actually frustrating because now I have two different products being clustered as being the same. I n actual fact, this is the medical application of the same automotive part. I n fact, the parts are identical so them being in the same cluster is not a problem. This part is rather unique. It's different to the other products in the same family, such that it got its own cluster with a few exceptions, so it's quite good. T hen the B2 and the B4 versions basically have the same design. W hat I'm concerned is that the B4 has been allocated a cluster 1 and also a lot of minus ones for wafers in the same product type. I'd like to further investigate what this might be due to so that I have scripted to the table, I want to make a subset of this SENSORTYPE NR SA AB4, and then I'm going to plot the differences for every parameter by cluster minus one and cluster one. H ere we see the parameters in question, and the biggest differences are observed for Orbot 1 and Orbot 2. I'm not going to get into the parameters themselves, but just suffice to say that some parameter differences are bigger than others. Now that I know that these exist, I'd like to check across all the wafers in this subset, how does Orbot 1 and Orbot 2 actually look? H ere we see, in fact, that the ones which have been allocated minus 1 are not belonging to the cluster itself have a much higher value of Orbot 1. In fact, this anomaly is a positive thing, because the Orbot value, the higher it is, the better. W e see that there's quite a large group of wafers having exceedingly larger values of Orbot than what we would typically see. T he next step, of course, would be then to do a commonality study to figure out how has this happened, where have the wafers been, what has the process been like, and look for an explanation. Well, we can see that very quickly, a multi product, multi- parameter evaluation of outliers or anomalies can be very quickly performed using this method. I will now move on to the second demonstration. Just need to open up another file. T his application is very different. I t's very much trying like... Or actually, it is a collection of functional data. In fact, there are bond curves, curves which occur in our anodic bonding process when we apply temperature, pressure, voltage across a wafer stack to have the wafer, the glass and the silicon bond together. If we look at individual wafer curves, we can see that each wafer has a similar but still unique curve associated with it. We can see the bonding process time and the associated current. T he task I would like to... The goal I would have, if I just remove the filter, I would like to know, without having to look through, in this case, 352, but we would have thousands of these every week, how many different types of curves do I actually have in my process? T hen tying that in with the final test data, can this curve be used to indicate a quality level at the end of the line? In order to do this, I'm going to split the data set. N ow I put the time axis across the top and the current through each column. The first thing that I do after doing this splitting then is to again go back to our PyAPI interface and I'm going to look at Split Data. W hat I want to do is to make a dimension reduction because you can see that I have many, many columns, and it would be much better that I can reduce the dimension here. A gain, I'm going to do a teach- me analysis. I'm going to send it straight to the server, and we can see that the algorithm has come back with two components. I can demonstrate them very quickly what they look like. T he 352 wafers which were represented by functional data, curve type data a few minutes ago are now represented using a single point for each wafer. Now, having reduced the dimension of the data, I'd like to perform a cluster analysis next. A gain, I'll go back to my AyAPI. I'm now going to do a HDBSCAN on the titanium components. I just need to check on this analysis what would be a suitable level. If I send it immediately, I get, colour code, the cluster, you can see that. Now clusters have been allocated to the teach-me components. This is the first level analysis using the teach-me, sorry, using the HDB defaults, I could, of course, try another setting. I could perhaps run, maybe, if we think out loud, 25 wafers, a batch of wafers, and half- wafer batches are things that would be of interest to me, and look to see what would this cluster now look like. N ow all of a sudden, I have much more clusters. O f course, it does take some subject matter expertise. You need to know what clusters you would expect. In this case, I said, okay, a natural rational group for us within the manufacturing would be a bunch of wafers, a lot of wafers, wafers are posted in 25 wafer batches. S ometimes we have halfway for batches, which we do experimental runs on and so on and so forth. N ow we can see that we have clusters associated with the different types of curves. I'm going to shorten this demonstration rather than you watching me do joins and so on and so forth. W hat I'm going to do is I'm going to take from the original data, I'm going to put this cluster into the original data. I t's of course, opening on another screen. I f I do cluster overlays, we can see... T his is the original data where at first I showed you each individual wafer bond curve. Now we can see that we were able to identify the distinct differences between seven clusters and one group of wafers which don't belong to any particular tester. W e can see that very quickly, we've been able to go through large numbers of wafers, determine similarities between them, and come up with clusters. If you bring this even one step further, we can take a look at the actual teach-me components, the coloured clusters, and have a quick look at what do the actual contents... W e can see this is cluster minus one. They seemingly have something which has a very high bond current at the very beginning, cluster zero, very high bond current at the end. Y ou can see that if we were to spend enough time on this, you would see lots of similarity between bond curves within each cluster. A short demonstration on how to take functional data from hundreds of wafers, cluster them, and with them the various visualization techniques within JMP, how to clearly identify and present so that people understand the different groupings that exist within the data sets. This concludes my demonstration number two. I have one more demonstration. This is maybe in some respects, for some, maybe a fun demonstration, so that longer to take... Again, it's not a real wafer, but I'm playing with the idea that I have a silicon wafer and there are some noise. This is a defect layout from an automated an inspection tool, this data has been simulated. The purpose of having this simulation is to look for scratches or patterns found from defect data layout. This is rather easy and straightforward if I don't have noise. I can see that there's noise associated with this data set. W hat I want to determine is, can I find a way to identify these three spirals, assuming that they simulate some scratch. In fact, they're not very similar to a scratch, except they are patterns having high- density defects in a small area. T hat's the main purpose of using it, rather than showing you actual wafer automated visual inspection data. The idea is that the task at hand is try to identify the spirals from this data set. I'm going to use, again, a trust ring method. A gain, it will be... The table I will use spiral data with noise. As Jarmo pointed out, we can run because if I don't know, obviously putting the number of wafers in here, 25 and 12 won't help me because I'm looking at a single wafer. T he numbers I put in should be somehow representative of how many defects are typically seen within a scratch and what are the smaller sample sizes associated with clusters and so on and so forth, minimum samples. Being a complete novice, I don't know. I'm going to put in some numbers to play with. Twenty five would be minimum cluster size with a minimum sample size of zero. Add to stack, and then I say, Okay, well, this is rather inexpensive to do so I'm going to add... You're missing the columns. Oh, sorry. Thank you. This will help. Let me clear stack in my enthusiasm to move forward. I did not include what I should have. L et me start again. Thank you. Twenty five minimum cluster size, minimum sample size, add to stack. Fifty minimum cluster, add to stack. Seventy five. I'm allowing the scratches to be bigger and bigger. Add to stack, 100. Are not necessarily bigger and bigger, but they would have more and more defects associated with them. Add to stack. And then I'm going to add another combination of 75 too, add to stack. I could just take one of these and run it. I could select one and run, but I'm not. I'm going to be greedy. I'm going to run the whole stack at the same time. I'm going to run one, two, three, four, five cluster analysis against the data that I've represented, I've taken it from this wafer. I send the whole stack, and cluster, something has gone wrong. All my clusters are showing minus ones. Let me try this again. To make a long story short, and also the fact that this is being recorded and we don't want to start again from the beginning. I know that at the end, that if I take this... I'm not sure why this has disappeared, but let me try it one more time. The table I need is the noise table. I'm taking HDBSCAN X, Y features, X, Y, 20. I'm going to make a shortcut, 75 and two, send immediately. Now, thankfully, I don't know whether I had selected incorrectly last time, the table or whatever. Now that we're here, put up a few thumbies, send it immediately, and so on. As I said, we could have run quite many. The idea then is to look then at the layout and try to determine. I s it with this particular setup, finding good clusters and it's a minus one? I t says, no, you're not finding anything there. T hen if I colour code by the other clusters, it has in fact found quite well lots of points that don't belong to any cluster. T hen three individual spirals which are very well identified. Y ou think, what's the benefit of this? Well, now that I know what typical scratch content looks like, then I could in fact, then open up another wafer. If I open up data from another wafer, make the plot of the layout, we can see that there are no scratches on this wafer, it's only noise. W hat would happen then if I run the same setup? My wafer is another wafer. I'm doing it on X, Y. I'm looking to determine based on my best settings of how I should be able to find scratches 75 and two, send immediately and plot with clusters. We only have minus ones, so nothing has been detected has been a scratch. H aving this possibility to be able to run this algorithm against wafers on the database, then I could make a collection of wafers that have scratches, don't have scratches, or spirals in this case, and then use that data for an input to a commonality study to try and determine which machines in the production line are coming, are resulting in the scratches on the wafers. This concludes the third demonstration. Now I'll hand it back to Jarmo. I'll take that. W e have a couple more slides to left. Here is a couple of ideas we have for possible future development using DoE approach for the stack building, basically what Philip did by hand, but used DOE, so I had middle max values and so on, and then sent that whole stack. Then metadata viewer, so you can compare the results, try JMP 17's new multiple HTTP requests, local server, so we don't rely on the server being up. Try the new hopefully updated native JMP Python Integration. This would allow us to have faster data transfer, possibly the more... W e could start testing with this application, then try, for example, running from graph builder, we could trigger the functions, combining different endpoints. F irst, we could input the data from t-SNE based on the input that data and then automatically cluster the t-SNE . T hen, of course, we're always adding new endpoints if we find out what we want to have. Last slide is that we will be sharing small sample of the code. There will be a JMP file with the JMP script, Python script, and installation instructions there. Y ou can try to be quite simple user interface which will send data to local server and you will get the data back. It also has some ideas in the instructions sheet that you can try to implement if you're interested in trying this approach for the JMP Python Integration. That's for us. Thank you. Thank you also from me. If you need to contact us, you can do so via the community.
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A Family of Orthogonal Main Effects Screening Designs for Mixed Level Factors (2023-EU-30MP-1272)
Saturday, March 4, 2023
Bradley Jones, JMP Distinguished Research Fellow, JMP There is scant literature on screening when some factors are at three levels and others are at two levels. Two well-known and well-worn examples are Taguchi's L18 and L36 designs. However, these designs are limited in two ways. First, they only allow for either 18 or 36 runs, which is restrictive. Second, they provide no protection against bias of the main effects due to active two-factor interactions (2FIs). In this talk, I will introduce a family of orthogonal, mixed-level screening designs in multiples of eight runs. The 16-run design can accommodate up to four continuous three-level factors and up to eight two-level factors. The two-level factors can be either continuous or categorical. All of the designs supply substantial bias protection of the estimates of the main effects due to active 2FIs. I will show a direct construction of these designs (no optimization algorithm necessary!) using the JSL commands Hadamard product and direct product. Hello. My name is Bradley Jones. I lead the team of DoE and reliability in JMP, and I want to talk to you today about a family of orthogonal main effects screening designs for mixed level factors. This is a subject which I'm really excited about. We've just submitted a revision to the paper for this and I'm hoping that it will get accepted so we can include it along with definitive screening designs in the DoE platforms in JMP. L et's get started. My collaborators for this work are Chris Nachtsheim, who's a Professor at the University of Minnesota Carlson School of Business, and Ryan Lekivetz, who's a member of my DoE team at JMP. Here's my agenda. I'm going to start with a little bit of history and some technical preliminaries. Then I'm going to describe three different constructions for these orthogonal mixed level screening designs. T here are three different ways that we can make them. I'll show you the JMP scripting language for creating these design sets. That will only be necessary until we can get them built into JMP itself. Then I'll spend a little bit of time looking at the design properties for designs constructed under these three methods. I'll discuss data analysis for these designs and show an example in JMP, and then I'll make a summary and some recommendations at the end. Let's start with some history and motivation. The first screening designs were fractional factorial designs like apartment designs, or non regular fractional factorial designs or regular fractional factorial designs. For these designs, every factor was at two levels only. En gineers that I have talked to have felt uncomfortable about these designs because they felt that the world tends to be nonlinear and two levels just isn't sufficient to capture nonlinearity in the effect of a factor in a response. Then in 2011, definitive screening designs arrived and here all the factors were assumed to be continuous and each factor was at three levels, which allows you to fit curves to the relationship between factors and responses. A t the bottom there is the citation for or the reference for the paper that first introduced these designs in 2011. Now, there are some pros for our initial implementation of DSDs and also some cons. Let's go through the pros first. The pros are that at least in our original implementation, six factor, eight factor and 10 factor definitive screen designs had orthogonal main effects, but we were unable to get orthogonal main effects for more factors. It turns out that a year later, somebody published a nice way of getting orthogonal designs for definitive screening experiments for every even number of factors for which conference matrices were available. That was a big advance. Another good thing about DSDs is that the main effects are orthogonal two- factor interactions so that the estimate of a main effect will never be biased by any active two- factor interaction. The really exciting aspect about DSDs is that all the quadratic effects were estimable, which is never possible with screening designs. Even with centerpoints, you can detect that there is a non linear effect, you just don't know where it's coming from. Then finally, in DSDs, if there are six factors or more, and only three of the factors turn out to be important, then at the same time you do screening, you can also do response surface optimization. You could maybe get, if you're lucky, a screening design and a response surface design t o optimize a process in one shot. You have to be lucky, of course, you have to have three or fewer active effects or factors. The cons of the initial implementation of definitive screening designs is that first, they couldn't accommodate categorical factors. Secondly, they couldn't accommodate any blocking. Thirdly, some researchers have pointed out that for detecting small quadratic effects, that is, quadratic effects where the size of the effect is about the same order as the error standard deviation, there is low power for these estimates, detecting them successfully. Of course, if the quadratic effect is big, then of course you can detect it, especially if it's three times as big as the error standard deviation. Now, after the original publication of DSDs, we were well aware that it was a problem that we couldn't accommodate two level categorical factors. So we wrote a new paper in Journal of Quality Technology in 2013 that showed how to incorporate 2- level categorical factors. Then in 2016, we wrote another paper in technometrics that showed how to block definitive screen designs using orthogonal blocks, blocks that are orthogonal to the factors. W e were trying step- by- step to address the cons that were associated with original implementation of this methodology. Another thing that we noticed was that people were having a little bit of trouble knowing how to analyze definitive screening designs. We invented an analysis technique that was based on our understanding of the structure of a DSD. It took particular advantage of the special structure of a DSD to make the analysis sensitive to that structure. Rather than trying to use some generic model selection tool like stepwise or lasso or one of those. This made it possible for a non-expert in model selection to use this out of the box automated technique for analyzing a definitive screening design. That was in 2017, but there's still problems. First, we did write that paper that added categorical factors to a DSD, but if you had more than three, the quality of the design went down, and that was undesirable. In fact, if you had too many categorical factors, things didn't look good at all. That was an issue. A gain, quadratic effects have been pointed out to have low power if they're small. The purpose of this talk is to introduce a new family of designs that addresses these issues. H ere we go. First, I have to do some technical preliminaries to explain what we need to have the ability to do in order to build these designs. I'm going to start out by talking about conference matrices. The conference matrix is the tool that the second paper that discussed DSDs in 2012 and introduced all the orthogonal DSDs for twelve factors, 14 factors, 16 factors, and so on. They use conference matrices to make that happen. I need to show you what a conference matrix is. You can see here, conference matrix, that is four factors with four runs, and there are zeros on the diagonal elements and ones and minus ones off the diagonal. T he cool thing about a conference matrix is that if you multiply the transpose of conference matrix with the conference matrix, you get an identity matrix times the one minus the number of rows in the design. I t's an orthogonal design. Now, conference matrices exist when the number of rows and columns is equal. They're square matrices and they only exist when the number of rows and columns is an even number. There's a conference matrix for every even number of rows and columns from two rows or and two columns to 30 rows and 30 columns, except for the case where the number of rows and columns is 22. It's actually been proven that the case where there are 2 2 rows and colum ns, the conference matrix has been proven not to exist. So there's no way to construct one, sadly, although I can't prove that result myself. Okay, the next thing I need to talk about is something called a Kronecker product, which uses that circular symbol with an X in the middle of it, so that when you see that in an equation, it means you want to make a Kronecker product. The Kronecker product is also called a direct product, and in fact, JMP scripting languages, language JSL makes Kronecker products of matrices using the direct product command, not the Kronecker product command. The Kronecker product of a vector one stacked on top of negative one with a conference matrix stacks C on top of negative C as below. What the Kronecker product does is for every element in the first matrix, it substitutes that element times the second matrix. So one times the conference matrix is just the conference matrix, and negative one times the conference matrix is negative of the conference matrix. Basically, a Kronecker product of one minus one with a conference matrix just stacks the conference matrix on top of itself. So here's a case where I did just that. You have a four by four conference matrix on top of its fold , which is also four by four, and if you were to add a row zero, you'd have a four factor definitive screening design. Conference matrices are useful, Kronecker products are also very useful for constructing designs, as it turns out. I have a few more preliminaries to go over. Let me talk a little bit about Hadamard matrices. Hadamard matrices are also square matrices, but they're constructed of ones and minus ones. We have Hadamard matrices built into JMP for every multiple of four runs or four rows and four columns up to 668 rows and 668 columns. So every multiple of four, we can support a Hadamard matrix. That well known. Hadamard designs are the Plackett–Burman designs and the two level fractional factorial designs. These are both Hadamard matrices. Hadamard was a French mathematician who lived in the late 19th century, and he invented this idea. I f I had my matrix as m rows, it's transposed times itself. Is m times the identity matrix. T hat means the Hadamard matrix is orthogonal, number one, and number two, it has the greatest possible information about the rows and the columns in the matrix that's possible, given that you're using numbers between negative one and one. They're very valuable tools for constructing designs. Now we have everything that we need to show how to construct these new designs. We call them orthogonal mix level designs or OMLs. They're mixed level because half of the columns or half of the factors are three levels. Therefore, the three level continuous factors and half of the columns or factors are two levels, and they're for categorical factors or for continuous factors for which we're not worried about nonlinear effects. Here's the first method for constructing one of our OMLs. If C sub K is a k by k conference matrix and H sub 2k is a 2k Hadamard matrix, so H sub 2K is a Hadamard matrix which has twice as many rows and columns as the conference matrix has. Then if we stack a conference matrix on top of its foldover and then replicate that, we get this matrix DD, which is just C negative C, C negative C, all stacked on top of each other. DD is two DSDs stacked above each other, minus the two center runs that DSDs normally have. Now, HH is because DD ends up having 4k runs because there are four conference matrices with K rows and columns stacked on top of each other. So there are 4k rows in this design and K columns. HH is just Hatamard matrix stacked on top of its fold over design. Since H has two k rows and columns already, stacking it on top of itself makes it have 4k rows just like the DD matrix has. It turns out that you can just concatenate these two matrices horizontally to make an orthogonal multilevel design. The DD part of it all has three levels per factor. And the HH part of it has two levels per factor. And you can see that there are k, three level factors and two k two level factors. Therefore, a 4k row design you can have as many as 3k columns. For example, if your design K was six, you'd have 24 rows and 18 columns. Six of the factors would be three levels and twelve of them would be two levels. Now you have way more two- level factors and you haven't lost any of the features of the definitive screening design. The main effects of this design are orthogonal to each other. Here's an example where I constructed an OML from a 6 by 6 conference matrix and a 12 by 12 Hadamard matrix. You can see there are 24 rows in this matrix and 18 columns. The first six of them are the six, three- level columns, and the next twelve are the twelve two- level columns. Now, of course, you don't need to use every column of this design. You could still use this design even if you had say, four or five three level factors and seven two level factors. You just remove five of the two level factors and a couple of the three level factors, and it's sort of arbitrary which ones you might remove. Here's the second construction approach here. C sub K is a K by K conference matrix, and H sub K is a K by K Hadamard matrix. Now we're going to create DD the same way we did before. DD is just a definitive screening design stacked on top of itself, minus the two synergies. HH is a replicated Hadamard matrix on top of the same Hadamard matrix folded over twice. Now if you look at the two columns of ones and minus ones, you might notice that those two vectors are orthogonal to each other. That's what makes this particular construction really, really powerful. In this case, the design has 4K rows and only two K columns. K of the factors are at three levels and K factors are at two levels. The number of runs in this design is twice the number of columns. But that's still a very efficient number of runs given the number of factors. It's the same effect of definitive screening designs in fact. Definitive screening designs have twice as many, twice as many plus one runs than factors. Here's an example created using a 4 by 4 conference matrix and a 4 by 4 H adamard matrix. When you stack them on top of each other four times, you get eight columns and 16 rows. Columns A through D, you can see are three levels because you can see those zeros and there are four zeros in every column. I should point out that if you had a definitive screening design, there are only three zeros in each column. Having an extra zero makes the power for detecting a quadratic effect a little higher than for the definitive screened design. That's the second construction method. The third construction method is very similar to the second, except that you have two different ways of adding Hadamard matrices to the example. Here we have the DD part is the same as the first two construction methods. The HH part, there are two HH things. One with the vector 1, 1 -1 , -1 , and the other one with the vector 1, -1 , -1, 1 . The three vectors that are ones and negative ones are all orthogonal to each other. That yields an orthogonal main effects design. In this case, the third construction again has 4K rows and 3K columns, that is K factors at three levels and two K factors at two levels. Those are the three methods. Here's an example of that construction with using a 4 by 4 conference matrix and a 4 by 4 Hadamard matrix. The result is a twelve- column design with 16 rows. Twelve factors in 16 rows. Very efficient design for looking at twelve factors. It's also orthogonal for the main effects. Main effects are orthogonal to two- factor interactions. Now I want to show you three scripts for creating these designs using JSL. In the meantime, before we drop this methodology into JMP, you can create these designs with a very simple JSL script. The first command is creating a conference matrix, in this case conference matrix with six rows and six columns. Then D is the direct product of the vector 1, -1 , 1, -1 and C. That gives you the matrix DD that we saw in our constructions. Then eight is a Hadamard matrix with twelve rows and twelve columns. Notice that twelve is two times six. We were requiring that for the first construction, the Hadamard matrix has to have twice as many rows and columns as the conference matrix. We make HH by multiplying 1, -1 by using the direct product, the Carnegie Product Construction. That gives you a 24- run HH design. The H thing has 24 runs and twelve columns. Then the last step is to horizontally concatenate D and HH to produce ML which I just shortened, shortened OML to ML. Then the as table command makes a table out of that matrix. The OML that we just created has 24 rows and 18 columns. Six of the columns are factors at three levels, and twelve of the factors are at two levels. Now, the six in the first line can be replaced by eight, 10, 12, 14 up to 30, except for 22. The twelve in the third line must be twice whatever number you put in the first line. You can use this construction to create all kinds of OML designs just by changing the numbers in the first and third columns. Here's the second construction method script. I start again with the conference matrix. This time I'm doing a conference matrix of four rows and four columns. D is a direct product of 1, -1 , 1, -1 and C. That's the same as before. This time I make H be a Hadamard of four instead of twice the number in the first line. I have to have a vector with four elements to direct product with H. I use 1, 1, -1, -1, use the chronicle product of that or the direct product in JMP and JSL speak. I get a design that has 16 rows and eight columns by horizontally concatenating that double vertical line thing horizontally concatenates two matrices and then the S table makes the table out of it. This second construction has 16 rows and eight columns. There are four factors at three levels and four factors at two levels. The four in the first and third lines can be replaced with any even number for which a conference matrix exists. I need to correct myself. The conference matrix has to be a multiple of four in order for this to work, because the Hadamard matrix is a multiple of four. Here's the last construction method. Again, we have a conference matrix of four, but it could be four, it could be eight, or twelve or 16. We make a direct product of this vector of ones and negative ones and C to get the replicated definitive screening design. Here we create a Hadamard matrix of four, but we have two different direct products. The first one where we're making a chronicle product of 1, 1, -1, -1 with H, and the second one we're making a chronicle product or direct product of 1, -1 , -1 , 1 and H. Those are two different matrices and they happen to be orthogonal to each other. Then we horizontally concatenate all three of these matrices and make a table from that. This design now has 16 rows because it's four runs in the conference matrix times four. You have 16 rows and twelve columns. There are four factors at three levels and eight factors at two levels. Here are three very easy JSL scripts. To make these designs, I'll put JSL into the... When it goes into the JMP community, I'll add the JSL. I'll also add several examples of these OML designs that you can use. Now I want to talk about a little bit about the properties of these designs. Here we see the design properties for method one and the colour map and the correlations shows that there are no correlations between any of the 12 factors in this or actually 18 factors in this design. The three- level factors are about 10% less efficiently estimating than the main effects than the two- level factors. That's because of the zeros in each of those columns. That doesn't help you, the zeros don't help you estimate main effects. Now I want to show you the alias matrices for this design construction method. You can see that there are a lot of main effects that are uncorrelated with two- factor interactions, but there are also a lot of main effects that are correlated with two- factor interactions. The three- level factors main effects are not alias with any of their two factor interaction. The same is also true of the two- level factors. Their main effects are not alias with their two- factor interactions because both sides of this design are constructed from fold- over designs. W e see that there's quite a bit of potential aliasing of main effects from active two-factor interactions. In some sense, method one is a little riskier to use than the other methods. Here are the design properties for method two. You can see that here I'm making a design that has 16 factors and eight columns, I mean 16 rows f or eight factors. The three- level factors have 15% longer confidence intervals than the two- level factors. Again, that is because those four factors all have four zeros. Four of the 16 runs are zero instead of 1 or -1. The cool thing about the second design construction is that none of the main effects is correlated with any two- factor interactions. That has many of the desirable effect or characteristics of a definitive screening design. There's a lot of orthogonality between pairs of two- factor interactions, but there are also some correlations. You can see here are some correlations, here are some, and so on. Finally, the design properties for method three show that three- level factors are a little less efficiently estimated than two -level factors 15%, 15.5%. We can see that there's some aliasing between main effects and two- factor interactions, but not as much as for the design construction number one. In terms of risk, this accommodates more factors with less risk than the first construction method. I'd like now to compare DSD to an orthogonal main effect or mixed- level design. You can make a DSD with eight factors, eight, three- level factors, and that would have 17 runs. I f you get rid of the centre run, you would have a run that's directly comparable with a multi- level 16- run design that you've seen in design construction too. Now, if we compare the efficiency for estimating main effects, the definitive screening design is only 91% D efficient with respect to the mixed- level design, the G efficiency is 92%, the A efficiency is roughly 90% and the I efficiency is roughly 82%. Y ou can see that the fraction of the design space plot shows that the curve for the mixed- level design is below the curve for the definitive screen design pretty much everywhere. This design is clearly preferable to the defendant screen design for estimating main effects at least. Now I want to talk about data analysis and use an example. I've created a design using the second construction and I created a Y vector by adding random normal errors to a specific function with both main effects and two- factor interactions and rounding it to two decimal places. The true equation, the true function is this one here. It has A, B, E and F main effects are all active and the AB, BE and EF two- factor interactions are all active. That's a function without error. I added normal random errors with a standard deviation of one. What I used was since this design can be fit using the Fit Definitive Screening Design platform within JMP, that's what I used. Here you see that the Fit Definitive Screening Design finds all seven real effects and doesn't find any spurious effects. It gets the exact correct set of effects. The deviation between the true parameter values and the estimated parameter values are pretty small. For example, the true parameter value for factor A is 2.03 and its estimate is 2.45 plus or -35. That's one standard deviation so it's just a little bit more than one standard deviation from its true value. Here that the true value of the coefficient of B is 3.88 and I get 3.94, which is very close to its exact correct value. You can see for yourself the estimated value of the root mean squared error is 1.2 and the true amount of random error I added was exactly one. You can see again, this analysis procedure has really chosen the exact correct analysis. Now I want to do a little JMP demo that shows basically the actual by predictive plot that you see there below. Then this plot shows that the residuals don't have any indication of any problem as well. I'm going to leave PowerPoint for a second and just go to JMP. Here's my data, here's the function with no error. I can show you that, that's just this formula that I showed you in the slide. Then here's the data where I've added random error to each of these values with it. Then what delta is, is the difference between the prediction formula of Y and the Y with no error. These values are how far we missed the true value of Y for every point. If I do the Fit Definitive Screening Design of Y, I get what I showed you before and I get the correct main effects and also the correct two factor interactions. Then when I combine them, I get the correct model with the correct art, very close to the true estimate of sigma. If I run this model using Fit model, this is the actual by predicted plot I get. This is the residual plot I get. Here's the prediction profiler that shows the predictive value of Y. You can see that if you look at the slope of the line of B and see as I change the value of A, the slope of B changes. If I change this B, the slope of A changes, if I change E, the slope of F changes. This indicates interactions happening. If I wanted to maximize this function, I would choose the high value for each of the factors. Then one of the things I did was I created a profiler that where I look at this is the true function, this is my predictive formula and this is the difference between those two functions. This is the setting that leads to the largest difference between the predictive value and the true value of the function. That's what I wanted to show you in JMP and I'll move back to my slides now. Let me summarize, we've talked about definitive screen designs with their pros and cons. I then introduced the idea of a Chronicler product and showed how to construct these orthogonal multilevel designs in three different ways. I showed you the JSL scripts, I shared the JSL scripts for constructing these designs. You can use this script by changing the numbers in the first and third lines to make designs that with increasingly large numbers of runs. I talked about the statistical properties of these designs and particular showed that their orthogonality, but also in the case of design construction two, not only are they orthogonal for the main effects, but the main effects are orthogonal to the two factor interactions. Design construction two only exists for designs that have a multiple of 16 rows and columns, which is a slight disadvantage compared, there's more flexibility with the other approaches. Then finally I showed how to analyze these designs. Let me make a couple of recommendations. Design construction method two is the safest approach because of all of the orthogonality involved and the fact that the two factor interactions are uncorrelated with main effects. I pointed out already that you don't have to use all the columns. You can create one of these designed and then throw away certain columns in order to accommodate the true number of factors that you have. The advantage of doing that is that you will also get better estimates of the error variance. Then it's important to remember that the three level factors are for continuous factors only. It wouldn't make sense to have three level categorical factors for these columns because there are far fewer zero elements than ones and plus ones. A couple of more things. Quadratic effects it turns out, are slightly better estimated by an orthogonal main mixed level design than a DSD. But if you wanted to improve the quadratic effect estimation, you could add two rows of zeros to the continuous factors. Those would be like center points. Then for the categorical factor, you can choose any vector of plus ones that has plus ones and minus ones in it. Then the other, the second row would have just the fold over of the first row in the ones and minus ones. That is all I have for you today. Thank you very much for your attention.
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Sowing the Seeds of Love for DOE (2023-EU-30MP-1203)
Saturday, March 4, 2023
There is a big problem with how we are educating our future scientists: we tell them that you are only allowed to change one thing in an experiment and you have to keep everything else the same. When trying to learn about the effect of many factors on a process or system, it is much more effective to change all your factors simultaneously. But people rarely learn about this method, "Design of Experiments" or "DOE." Or they only hear about it later in their careers when they are resistant to new ideas. The result is a huge waste of time and resources due to inefficient experimentation. In the summer of 2022, JMP® launched a competition with an engaging and simple experiment to demonstrate the power of DOE. In this presentation, you will hear from the contest winner and the designer of the experiment. You will hear how experimenters of all ages can get their hands dirty growing garden cress under different conditions according to a statistically designed experiment. And you will see how the results can be easily analyzed with compelling visuals, as well as using sophisticated Functional DOE analysis in JMP® Pro. I'm Phil Kay and I'm joined by Weronika, and we're going to talk about a fun experiment that we set up as a competition with the idea that it's sowing the seeds of love for design of experiments. And it's all about growing cress, which I'm sure many of you will have done when you were at school or at home. And there's a problem, I think, in how we educate our young scientists. T his is taken from the British Broadcasting Corporation's bite size for Key Stage 2, so for young scientists. The curriculum in the United Kingdom, at least, tells people about this fair test idea. And that is when you are testing something, you need to make sure it is a fair test. To do this, everything should be the same except the thing you are testing. So we're only allowed to change one thing at a time. And that's not ridiculous. It's not necessarily wrong, but it's not all of the truth as well. It's not necessarily the best way when you come to experiment in commercial R&D and industry. T he consequences of this, if we accept that we're only going to test one thing at a time, let's imagining we're experimenting to understand what affects the height of garden cress, and we want to understand what's the effect of light conditions, sunlight or dark. We're pretty sure that's going to have an effect, but we'd like to understand what it is. What's the effect of the growing medium whether we grow on soil or on cotton wool? Again, we think it's probably going to have an effect. We'd like to experiment to understand or quantify the effect. T he fair test way of doing this would be to take control conditions. So we grow some cress in sunlight and on soil. And then we do a fair test. We just change one thing. So for Fair test 1, we change the growing medium to cotton wool, and we see what the effect is. For Fair test two, to understand the effect of light conditions, we change to dark and we keep everything else the same. We keep our other factor the same. T his would be fine, fair tests would be fine, except nature doesn't necessarily play by those rules. Nature doesn't play fair all the time. And what we should really be doing in this situation is a designed experiment. And in this case, we would test all possible combinations. W e wouldn't just be changing one thing at a time, we'd make sure we tested all the possible combinations , we changed all the factors according to a strategy. And what this enables us to do is gain a richer understanding. So we can understand things like interactions between factors. And for this cress experiment, we can see the height at day five after five days of growing, we're looking at the effect of light condition, sunlight or dark. And we can see that the effect of light, whether it's sunlight or dark, is dependent on the growing medium. F or soil, we are seeing a bigger difference between dark and sunlight than we are with cotton wool. This is an interaction. We can only understand these interactions when we use designed experiments, and these are often critical in commercial R&D. So what we need is some fun ways of introducing these ideas to young students, to students of any age. Now, let me go to a digression where we got the name of this talk from. It's from a song by a group called Tears for Fears. It's not a very new song, so if you're young, you may not have heard it. If you're a bit older, you'll probably know it because it was nominated for the best postmodern video at the MTV Music Awards 30 odd years ago, whatever best postmodern video means. The first line is, high time we make a stand and shook up the views of the common man. I don't know if I like that first line very much, but I think it's appropriate here. We'd like to shake up people's views about how we should do experiments, how we should change the factors in an experiment. I was a little bit concerned that this is a British band that people may not have heard of Tears of Fears, may not have heard of this song. So I looked at the data and actually I found that it was a worldwide hit and particularly big in Canada. It reached number one there in 1989. The Cress experiment, how did this start? Well, my colleague, Michael, in marketing here at JMP, wondered if we could make a fun experiment out of growing garden cress. That hadn't occurred to me. When I first heard this, I thought, that's a brilliant idea. What we wanted to do was create an experiment that's simple enough for anyone, for experimenters of all ages, young experimenters, old experimenters. We wanted it to be simple enough you could do it at home. One of the challenges with coming up with good examples of design of experiments is, science is generally expensive. Measuring the outputs of your scientific experiments often requires really expensive instruments. So we wanted something that was simple and cheap to do. And we wanted it to be an interesting way, just a fun way to introduce the key concepts of statistical design of experiments. W e didn't want it to be difficult, we didn't want you to have to do lots of very complex analysis. We wanted it to be very immediate and fun way of introducing these ideas. I did some experiments in the Kay Family Research Kitchen here with some assistants. I had my eight- year- old daughter and my 15- year- old daughter help me with this. My 12- year- old daughter was too busy watching DOC, I think. And it was very successful. They had a good time doing it, I think, and it started some interesting discussions. W e did this experiment and we set up the experiment so that we were growing some of them in soil, some of them in cotton, some of them in dark, some of them in light conditions. And my eight- year- old child said, "Well, Dad, it would have been easier if we just put all the soil ones in the dark and all the cotton ones in the light." I didn't say anything, so I wait for my 15- year- old to respond. She said, "Well, but then we wouldn't know if it was the soil or if it was the dark that had the effect." This is a beautifully concise way of describing confounding. This was a very proud moment for me as a parent that one of my children could explain this concept of confounding in a much more succinct way than I have ever managed to do. And we got great data. T he 15- year- old lost interest after we'd set it up, but my eight- year- old daughter carried on with the experiment, observing it over a number of days. W e measured the height of the tallest plant in each pot, actually within each compartment of an egg box. W e measured those and she took all the measurements and we got some really good quality data. Let me just show you first of all, though, the actual experiment. T hree factors, we tested substrate, soil or cotton wool, the light conditions, dark or light, and we used plain or curled cress types. We got two different types of cress seeds. And this is a two- to- the three full factorial for those DOE nerds out there. And we've replicated on the two to the three minus one half factorial there. So that gives us 12 runs, 12 pots, which works well because in the UK at least, egg boxes generally come in sixes. So we could use two egg boxes to do these 12 runs. And as I said, the data was very good. We can do some simple analysis. This is one of the things I like about it, is that we can just look at the ones that were grown in the light and the ones in the dark and see how the height is different after seven days. And it's very compelling, there's a very big difference. It wasn't really the difference that I was necessarily expecting, and it was an interesting surprise to all of the experimenters involved. W e can do some simple analysis, just some simple visuals. Let's just plot the heights versus light conditions. And again, you can see the big effect there, big effect of substrate, very little effect of cress type there. So introducing these simple analysis, and then we can obviously take it to a greater level of sophistication, build a full statistical model. And that brings us to the profiler, which I think is just such a great way of understanding design of experiments and statistical models. Very powerful, compelling way to understand the effects of each factor and interactions between factors as well. I f we look at day seven, we can see there's an interaction between light conditions and our substrate. And we can take it to an even greater level of sophistication because this is actual functional data. I f you're interested in Functional Data Explorer, well, this is a great example data set because we're collecting the height data as a function of time for each of the runs of our experiment. W e can use Functional Data Explorer and Functional DOE to understand how the factors affect the shape of this growth curve. We can see the rapid growth with soil versus cotton wool. We can see the rapid growth, increased rate of growth in the dark, and actually the fact that it's starting to die off towards the end of the experiment here. I was really delighted with how the experiment went. It was very simple to do, very compelling, really accurate results. It's so hard to find experiments that people can do at home where they can get an accurate, continuous quantitative response out that they can measure just with a plastic ruler in this case. We went ahead and did this as a competition. I wrote a blog post about it, which we'll provide the link to that as well. W e ran this last summer, summer of 2022. I'm going to introduce next our competition winner, Weronika. I've also done some visuals of Weronika's results in JMP Public. We'll share the link to that as well so you can actually see Weronika's data, download the data yourself if you log into JMP Public and see the results for yourself. But now, Weronika is going to show you what she found in this cress experiment and the impressive results that she got that meant she was the competition winner. I think you're on mute, Weronika. Thank you, Phil for introducing me. I would like to share with you my experience in a competition, my experience regarding design of experiments, regarding the planting of the cress. T he main aim of the challenge was to introduce design of experiments to researchers, to engineers, to students, to any of the people. But also in that experiment, we have to check with design of experiments what factors has influence on the health of the garden products. T hen defined factors by the organizers, Phil , there were three factors. It was the surface. W e use cotton wool, and g arden soil. Then the second factor was light conditions. So we plant garden cress in sunlight and in the dark. And also we had to check what influence has been soaking on the height. What was my first impression? As Phil said, they wanted experiment to be simple enough for everybody. But I was not so convinced at the beginning because when taking a look at my previous experiments with planting, it was not so good. So I didn't expect that my garden cress acted in different way. And I wasn't mistake, I wasn't wrong. My first results were good. First of all, I put so many seeds in one spot that the pre- soak samples become a shell. Some kind of shell. They didn't germinate , so I don't receive any plants. Moreover, my egg box was broken by the water, which can be seen here. It was broken. Also, some marker was destroyed and I saw no numbers of spot. And also the soil migrated from one hole to the adjacent one. I t was mixed with the cotton, especially when I put the waters on the soil, it was not good. After my first failure, I drove some conclusion why I received a failure. First of all, I decided to use plastic espresso cups instead of the paper cups because plastic is better than water. Use less number of seeds in each hole. Don't put as many as I can, but do it smartly. And also in this moment, I come to idea to maybe add the fourth factor to my experiment, density of the seeds. Also, I wanted to check not only how surface, light condition, and soaking influenced the height, but also the density of seed. I set two levels, low and high. In low density, I use 20 seeds and evenly spread them in a cup. In high density, I took 40 seeds and try to put every in the middle of the cup, so it's [inaudible 00:16:09] . M y design had four factors. Each factor had two levels. I used a full factorial design as a design time. I received 16 number of treatments, 2 to the power of 4. I decided to replicate eight treatments in order to receive variability and be able to estimate some standard deviation and so on. I n total, I received 24 test runs. Experiment was done in August when it was very warm, so it was nice weather for a planting and being a gardener. Okay, those are my results. Here we can see design table with all factors, test runs 24. Here I put the height after three, five, and seven days. In that table, you can see the factor effect estimates after seven days. We can end with the bold font. I marked variables, factors which I found to be statistically significant, and it was surface, light, density. Soak occurred not to be important, but alone as a main effect only. It occurred to be important in two factor interactions. T he interaction between surface and soak and the light occurred to be significant, so we cannot assume that the soaking is not important. Also, two three-way interactions were significant, four- way interaction not significant. N ow I would like to present you some pipeline and some steps which I used in my design experiment. I think that it's quite a good approach which everyone can use in the experiment. First of all, we have to generate the design. As a first step, we shall define what factor we want to check and what levels. And when we set it, we have to choose design type, because usually choosing the type is dependent on the factors, how many factors we have, or is only two or three, or maybe we have no factors, how many levels. D efine number of replicates we have to include, and then we can generate this table with which in JMP is very quickly and convenient. When we have a table, we can run experiment, collect data, put in a table. When we have everything, we can go to the next step, estimation of the factors. We formulate the full regression model and estimate factor effects. So we check which factor is important. Here you can see the main effects plots, two- way interaction plots, three- way interaction plots after seven days. What it's worth to mention is that interaction. This is what Phil said, that the interactions are important. They happen in the real world. And here it's a good example. F or example, when we have cotton at the surface, it's better to use no soaking. If you use soil, it's better to pre-soak samples. And this is when we would check only one factor at a time, so for example, take soil. And with soil, we would receive that presoking is better. With a cotton , we would use also pre-soaking, but in that case it's not true. T his is the beauty of the interactions. And that's why we have to take into consideration their health. Then, statistical test. C hecking which effect is important. In JMP, we can also see parameter estimates, the effect tests, and conclude which is significant. W hen we see which are not significant, we should redefine the model after dropping the non- significant effect and calculate estimate one more time, linear regression. In that case, linear regression . But we cannot finish on that, but we have to also check assumption that our model is correct, statistically correct. So we have to, for example, check the residuals for normal distribution. It can be done with the normal probability graph of residuals in a JMP. When we see the observations, residuals follow the straight line and are in the border range, it means that it's correct, it's normal distribution. But also we can check it with the test, with numerical test like Shapiro- Wilk test, to check if residuals follow normal distribution, and then mean test to check if the mean value is equal to zero. When we finished that, we can draw the conclusion. In my conclusion, in my experiment, was that the most important factor was light, and its effect was about eight times higher than the effect of the second most important. Plants cultivated in dark grow higher than those in the sun . The other significant factor was surface, and I obtain the result that the garden soil is better. In garden soil, the plants grow higher. Also, the fourth factor which I added, sowing density, also occurred to be important, but its significance increased over the time. After the three days, sowing density was not insignificant, but after five days it was significant, and after seven days it was even more significant. So it increased with time. A lso in general, during seven days, three different three-way interaction were significant, which suggests that all factors interact really together and we cannot interpret them separately. That all, sun, soil, water, everything in nature is combined and have some dialog inner dialog. Also, except of that, I checked different physical things, let's say. And cress cultivated inside light become green and developed big leaves. Whereas in dark, they were very yellowish, they were fragile. When I touched them, they broke down. E xcept that they were higher, but they were, I would say, not healthy. And also roots for plants cultivated inside light go longer. Here you can see inside… it's very difficult to see because roots are white and cotton is white. But you can see somehow that they are here rolling around the roots, and here there is just plain cotton. With soil, it's better to visualize because it's better to discern the s oil. And we can see that in some light, we have longer roots, whereas in dark, they are very short. And to maximize the height after seven days, we shall use soil, we should pre-soak samples, seeds, we shall put them in dark and use high density. Those are the picture of my results. We can see that throughout the experiments, samples in dark all the time they were yellow, they were thin, whereas in the sunlight they were healthy green and thicker. My conclusion regarding the design of experiments, my experience. Design of experiment is a great tool which can be used to optimize any process. Even something like cultivated garden cress can be fitted to the design of experiments. It helps to incrementally gain knowledge about the process. For example, like me at the beginning, I had no idea how the density influenced the height, but when I put so many things, I decided that I gain knowledge that it has influenced and I have to do something about it and also consider it. We can also increase our confidence about our our results, and that our results will be indeed statistically significant. So we will have no biases. We know that interactions are involved. Of course, some factors can be alias with others, for example, in factor design. But the advantage of design of experiment is that we are aware which one are confounded, and we can draw proper conclusion based on that. So if, for example, one pair of confound factors appears to be significant, but we don't know exactly which one, we know to what we have to focus on. And also, do not be afraid and disaffected in the first try to not be successful, treat it as a lesson and draw a conclusion why it happened. D on't give up, but sit, think, why I failed, what I can do in other way, what I can improve, and do it and try one more time. And design of experiment can bring fun with the proper attitude because this experiment really, really have fun. And as I said, it was August, it was very sunny, so it was nice weather, nice time to spending time on the [inaudible 00:27:04] . Thank you for your attention. Yes, thanks very much and thanks, Weronika.
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Revisiting Pearl’s Influenza Studies Using JMP® (2023-EU-30MP-1319)
Saturday, March 4, 2023
Roselinde Kessels, Assistant Professor, University of Antwerp and Maastricht University Chris Gotwalt, JMP Director of Statistical Research and Development, JMP Guido Erreygers, Professor of Economics, University of Antwerp In 1919 and 1921, Raymond Pearl published four empirical studies on the Spanish flu epidemic. He explored the factors that might explain the epidemic’s explosiveness and destructiveness in America’s largest cities. Using partial correlation coefficients, he tried to isolate the net effects of the possible explanatory factors, such as general demographic characteristics of the cities and death rates for various diseases, on the variables measuring the severity of the epidemic. In this presentation, we revisit Pearl’s data and apply variable selection with a pseudo-variable in JMP®'s Generalized Regression platform instead of Pearl’s correlation analysis. We use Poisson forward selection on the variables in the platform with AICc validation. We find that our results largely correspond with Pearl’s conclusions but contain some additional nuances that are substantive. This paper contributes to the literature showing that Poisson regression proves useful for historical epidemiology. JMP’s Generalized Regression platform offers researchers much flexibility in choosing the most appropriate model. Okay, welcome everybody to this presentation in which I will talk about Pearl's Influenza Studies. Pearl a biostatistician, he lived quite a while ago during, specifically, the Spanish Flu, and he was the first one to analyze the weekly data that was collected by the US Bureau of the Census about the Spanish Flu, which occurred in 1918, 1920, and this for the large American cities. Pearl, he was the first to do so to analyze the data regarding the Spanish Flu, and he wrote two reports about it, his influenza studies one and two, which we will revisit now during this presentation, and we'll see how we will be able to look into the data, analyze the data using JMP. This is joint work with Chris Gotwalt, who contributed to the methodological component, and Guido Erreygers, who initiated the idea of revisiting Pearl's influenza studies. The overview of this talk is as follows. First, I'll discuss a little bit the Spanish Flu, what it was all about. Quite a deadly pandemic at that time. Then I'll introduce Pearl's Influenza Studies, and I'll talk a little bit about the data that Pearl used in his analysis. We have added some census data from 1910 on top of that for our analysis, which consists of a variable selection procedure with a null factor, a random factor, an independent normal factor in our analysis, and bootstrap simulation from the data. Then we'll be discussing our results and compare those results with Pearl's results, and then we'll conclude. First of all, the Spanish Flu pandemic, to frame that a little bit, it was one of the deadliest pandemics in history, as witnessed by this list here showing the world's deadliest pandemics in overtime. You can see that the Spanish Flu here ranks fifth in terms of the number of deaths that it caused. T his list is headed by the Black Deaths, which killed about four times more people than the Spanish Flu did. Then below this list, you see COVID- 19 pandemic appearing, which we got all exposed to. It also appears in this list still. Just as with COVID- 19, gatherings were more encouraged to happen outside, and also at the times already of the Spanish Flu pandemic there, that was the case as well, and even for classes to happen outdoors, as you can see on this photo. The Spanish Flu pandemic consisted of three waves. The first one started in March 1918 in the US and spread to Europe and to the rest of the world. Then a more severe wave, the severest one started in August 1918 in France and spread rapidly to the rest of the world, as well as coincided with the end of the First World War. Then a third one which was less severe than the second one, but more severe than the initial wave, started at the early beginning of 1919 and hit some specific countries like Australia and Great Britain. Here you see the timeline of the three waves of the Spanish Flu pandemic occurring in the US, more specifically because we have Pearl's data from the US which we look into. The death toll was humongous and most deaths occurred actually in India and China. What was specific about the Spanish Flu pandemic was that many young healthy adults died as shown by a W pattern of mortality rates, which is here shown by the full black line. In contrast to the U shape of the normal seasonal influenza and pneumonia mortality rates which were registered prior to the pandemic. For seasonal influenza and pneumonia, this U shape shows that individuals younger than four years of age and people older than 75 years, that these were most hit by seasonal influenza and pneumonia. Characteristic of the Spanish Flu pandemic was that besides those two age groups, also the young adults in the range between 20 and 40 years of age got specifically hit by this pandemic, eventually leading up to a huge death toll. Then further onwards throughout history, epidemiologists and historians of epidemiology have applied statistical analysis to the Spanish Flu pandemic. More specifically, people worked on the US as well, namely, Markel and colleagues, they studied the effect of non- pharmaceutical interventions in American cities like school closure, cancelations of public gatherings. Mamelund studied the influence of geographical isolation of specific areas and specific regions in the US. Clay, Lewis, and Severnini studied cross- city variation of 438 US cities, but also elsewhere around the world, so not only in the US but also in Europe, like in Norway and Spain. Data about the Spanish Flu pandemic were analyzed together with census data and so forth. The Spanish Flu pandemic kept be intriguing for many researchers over time. About Pearl's influenza studies now, here you see the first of his influenza study, the beginning of it. First, he talks about the severity of the outbreak and actually that the death toll for the United States was set at about 550,000, which is about five times more than the number of people who actually died during the First World War. Hence, showing the severity of this pandemic and actually how deadly it was, how explosive it was. That was specifically what Pearl was interested in examining and relating to other variables. The second of his influenza studies looks as follows, so the beginning at least. That's an update of the first of his studies because he got some criticism after first study by some peers who criticized him for not being accurate with certain variables. He had issues with what we now refer to as construct validity. Some of the data were not really measuring what they were supposed to measure, so they were not so accurate and they could be defined more appropriate, more accurate in order that they really measure what they should measure. He did that. He tackled the data again, the variables. Set up new definitions of the variables and moved forward. The data for the first of his studies looks as follows. We have data from Pearl of 39 American cities. The response variable he wanted to study was the epidemicity index, which is given by the symbol I 5 in his studies. That was the first response variable he wanted to study and he wanted to also find other variables like demographics that could be predictive of this response variable. Now, this response variable was a measure for the explosiveness of the outbreak in terms of epidemic mortality, so how explosive the outbreak was and hitting the various cities in the US. He defined the peak time ratio, so the peak of the excess mortality rates divided by the peak date. In this way, he wanted to compare cities with one single very sharp peak to cities with a long flattened curve of excess mortality rates. He devised this epidemicity index himself. He wanted to relate this epidemicity index to various factors like the demographics. First of all, the population density for 1916, and then the geographical position, which is a straight line distance from Boston. He also included the age distribution, chi- square, which is an age constitution index showing the deviation in the age distribution of the cities from a fixed standard age distribution. He also studied the percentage population growth in the decade 1900, 1910. Besides the demographics, he also involved the death rates for 1916, so prior to the pandemic. First of all, a n aggregate death rate from all causes and then specific diseases, death rates for specific diseases, namely pulmonary tuberculosis, organic heart disease, acute nephritis and Bright's disease, or failure of the kidneys, influenza, pneumonia, typhoid fever, cancer, and measles. There was quite some correlation between these death rates. I already told you a little bit about the response variable that Pearl developed, namely the epidemicity index. That did not arise into just a single attempt, but he started actually from I 1. First of an epidemicity index that he further improved into I 2, I 3, I 4, and then he was happy enough to move along and to work with the I 5 epidemic ity index. To distinguish then cities with single, very sharp peaks to those with long, low flat curves of epidemic mortality. He updated the variables from his first study and the second study, and in that sense, he also updated, he also modified the epidemicity index and the new epidemicity index. He referred to that one as I 6. As another variable of interest, he also defined to this destructiveness variable as containing excess mortality rates. Then he wanted to relate these two responses in his second study with the normal death rates, which were the mean death rates over the three years, 1915 to 1917. Then he also brought in the demographics. Again, he modified the age constitution index based on the 1910 census data, and he used that. Then also he involved the sex ratio of the population of males versus females. The population density of 1910 is used in the first of his studies, remained in the second study. Then instead of a geographical position, he used latitude and longitude in his second study. Then lastly, he used the percentage population growth in the decade 1900, 1910, again. These were Pearl's data to which we added some additional census data from 1910 which are given over here. Instead of the age constitution index, we used the share of the different age groups, and the pure numbers actually, because we were not really happy with the way Pearl defined the age constitution index. It was really quite complex and not clear enough so that we thought to just go about and use the 1910 share ages instead of this age constitution index developed by Pearl. Besides that, we looked into the number of persons to a dwelling, the percentage of homes owned, the school attendance of population, 6 to 20 years of age, and the illiteracy in the population, 10 years of age and over, and see whether one of these factors or some of these factors could be predictive of the tree response variables in Pearl's studies. What was Pearl's analysis all about? Well, he got into multiple correlation. He studied all the data making use of partial correlation coefficients as well as the normal correlation coefficients of zero order, but he did that very rigorously. He had computed this all by hand and did this quite well actually and took into account various other factors in these partial correlations by having those other variables being constant. For the partial correlations and the other correlations, he also computed probable errors to find out whether these correlation coefficients were significant or not, so he did this quite well. Now, the analysis that we are using is the one in which we are going to actually select variables with a null factor and we are going to bootstrap our data. We are doing so because our P values for our data, which are unfortunately not orthogonal, they can become heavily biased. Since we are not really using nicely orthogonal data, the P values can become quite biased towards zero. It's always the danger with P values for observational data that unimportant factors all of a sudden become important and that the type one error rate is not under control. To solve the fact that the P values are not uniformly distributed anymore in the case of an unimportant variable, to solve that issue, we are going to involve a random variable, a null factor, an independent normal variable into the analysis and see to what extent it appears in our procedure of selecting variables. This idea is inspired by or originated from the JASA paper by Wu, Boos, and Stefanski in 2007. Specifically, what we have done is, well, we included a single null factor in the variable selection procedure and performed 2,500 bootstrap replicates for variable selection using JMP. Then we calculated the proportion of times each variable enters the model. Variables that enter as often or less than the null factor are ignorable, and variables that enter more often than the null factor, well, these are the ones that we are going to select as being predictive of the response variable. In JMP, we specified two new columns, and actually, one formula is needed. That's the formula for the bootstrap frequency column that you see here. The bootstrap frequency column is based on our null factor, which is an independent normal variable that's reinitialized each time during the bootstrap simulation. Based on this reinitialization, the frequency column gets updated so that we have a 100 % resampling of our sample size of our data with the fixed sample size as it is. Then for the variable selection, what kind of regression also would we apply that we could find out in the generalized linear model platform of JMP, because taking into account the frequency column also, if we do variance selection, we also need to do the distribution determination at the same time. It has to happen simultaneously. You can't do it apart based on the original data. Actually, the graph on the left- hand side is a little bit misleading because that graph contains the original data, but distribution determination you should actually do while doing the variable selection itself. Assuming a Poisson distribution, well, that assumption was not rejected, so we could actually move forward with the assumption of a Poisson distribution, which is also maintained in the literature throughout for mortality rates and so forth for analysis. It was not really rejected and it was good to assume such a Poisson regression for the analysis. Then also we applied Poisson regression in combination with variable selection for the epidemicity index I6. However, we switched to normal regression or less regression for the third response variable, the destructiveness variable. The way to apply this regression in JMP by means of variable selection is by using the generalized regression platform where we then define the response variable for analysis and put the frequency column into the freq box. Then as model terms in the construct model effects window, we involve all the variables, all Pea rl's variables, together also with our null factor or independent normal variable that we also included. Then we move forward with the regression procedure by selecting forward selection estimation method. As a criterion, we used the Akaike information criteria with the correction for small sample sizes to decide upon the final model to include the final model then to select with the selection of the variables. The solution part that you see here is based on normalized data to put them on the same scales, so scales and center data and also to diminish the effect of multicollinearity in the data because there's quite some. Then you see again, the original predictors popping up in the lower output. Then we select a model with the lowest Akaike information criteria. One after the other, the variables coming being selected by forwards variable selection based on the Akaike information criteria. As you can see in this output actually here, the null factor got into the model. The null factor completely unimportant, uninformative, so got into the model, which is not good, of course. We had to do this estimation method, this variable selection procedure 2,500 times, and we did this in JMP by right clicking on the estimate column and then hitting simulate and then selecting the frequency column as a column to switch in and out between the different bootstrap replicates so that initializations of the null factor were always being guaranteed between the different bootstrap replicates. Then finally, so we got the 2,500 model selections out of the bootstrap simulation. Then we computed the proportion that all of these variables got into the model or were given as non- zero estimates. We were represented by non- zero estimates. Especially, of course, we were interested in how many times the null factor appeared in the selection of the models. This turns out to be 41 % of the times, which is quite high. T hese are our new false entry rates, actually. A very high percentage in which the null factor got selected. We accounted for some upper bound, an upper 99.9 % confidence limit also that we took into account, and even a little bit higher sometimes. To be assured that the factors that you see in green that they appeared most often then in the model selection. As you will see also another example now too, where we got tied with the null factor as well. The factors or the variables in reds, they have not been selected since their occurrence is lower than the occurrence of the null factor over the different bootstrap simulations. The variables of interest here that got selected and are predictive of the epidemicity index I5 are the death rates of causes, death rate from organic heart disease, the death rates from pneumonia, death rate from cancer, death rate from measles, and the geographical position. Now, the death rate from all causes, that's an aggregate for the death rates from the individual- specific chronic diseases. We were also interested to see what would happen if we were to take this out of the analysis. This is what we did on the following slides. We took it out, death rate all causes, and then we got a different picture. The death rate from measles and the death rate from cancer turned out to be unimportant now, whereas death rate from pneumonia got in the green zone, as well as the death rate from pulmonary tuberculosis. These were also quite highly correlated with the death rate of all causes. Death rate of all causes masked. These variables, although some of the death rates are also correlated among each other, so we have to be careful here. That was a new result that we saw. Now, eventually, we repeated these variable selections further onwards with only the variables in green that you see here on the screen, so the ones that got selected to which we added our new 1910 census data, like the shares of ages and the illiteracy and the schooling and so forth. We did this with and without dead rate or causes to finally then select the variables that were common kinds of overall analysis. All the different analyses selected some other variables still, but still there were some variables which were present all the time, and these are the ones that we finally then contained into the model, which is our final Poisson regression model that we obtained in the end. Which contains death rates from heart disease, organic heart disease, death rates from all causes we kept telling, as also Pearl stressed that as being quite important. Share ages, 0 to 4. School attendance of the population, 6 to 20 years of age and the geographical position. That was our final regression model for the first response, the epidemicity index I5, and below you see the results of Pearl. Having done a correlation analysis, he was able to identify pulmonary tuberculosis, organic heart disease, and acute nephritis and Bright's disease. Besides the death rate of all causes that he deemed quite important, which was actually also always the case for our analysis. It always came on top of our analysis. We kept it in. Now, Pearl also pointed towards some specific chronic diseases, but we were not able to put these on top. We did not find them to be stably present. Overall, our analysis, so we did not identify them. Also, Pearl, actually, after his first study, got criticized because of pointing towards these individual diseases. Then in the second of his study, also, he was more prudent, more conservative, and only pointed towards the death rate of all causes and the organic diseases of the heart as the ones that are predictive for his modified index of the explosiveness of the outbreak I6. Our final analysis is the one that you see here pointing towards also death rates from organic heart disease, death rate from all causes, the share of the ages between zero and four, and the population density rule. W e're always able to see the population density coming up high in the list of variables that occurred almost continuously over the bootstrap replicates. That also turned out to be an important variable from our analysis. With the destructiveness variable, we did not find all that much of variation. The range actually of values was not as large as for the other two responses, the epidemicity index . We were only able to identify a few variables and specifically the death rates coming from the organic heart disease. Then we identified, besides the share of the youngest people between 0 and 4 years, also the share of people ranging between 20 and 40, so 25 and 44 years of age. The healthy people actually prior to the pandemic, that was also indicative of excess mortality rates over the different cities in the United States. Pearl's data sets, as we could see also, we only had very little data. There were very tiny 39 observations in the first study and in the second study only 34 observations because they modified some of the variables, some observations got lost. The data are also observational with quite some multicollinearity involved. The quality of the data could have been better. Therefore, our analysis for sure are useful, but they are not magical, as well as Pearl's correlation analysis. Specifically, his first analysis, it was not supported. He knew afterwards that people did not really support his first analysis. Anyway, as George Box said, "All models are wrong, some are useful." We were able to select satisfactory models in a sequential manner. First of all, we included Pearl's variables and retained the selected variables, to which then we added new 1910 census variables to finally select those variables that are informative and each time with and without the death rate from all causes. Then we retained the variables that popped up in the green zone to be quite predictive of the response that popped up all the time, so over all the analysis that we did to then have the models that I presented. I hope this was informative for you to listen to and many thanks.
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Do You Trust Me? Assessing Reliability for Driver-assisted Autonomous Vehicles (2023-EU-30MP-1235)
Saturday, March 4, 2023
Autonomous vehicles, or self-driving cars, no longer only live in science fiction. Engineers and scientists are making them a reality. Their reliability concerns, or more importantly, safety concerns, have been crucial to their commercial success. Can we trust autonomous vehicles? Do we have the information to make this decision? In this talk, we investigate the reliability of autonomous vehicles (AVs) produced by four leading manufacturers by analyzing the publicly available data that have been submitted to the California DMV AV testing program. We will assess the quality of the data, evaluate the amount of information contained in the data, analyze the data in various ways, and eventually attempt to draw some conclusions from what we have learned in the process. We will show how we utilized various tools in JMP® in this study, including processing the raw data, establishing assumptions and limitations of the data, fitting different reliability models, and finally selecting appropriate models to draw conclusions. The limitations of the data include both quality and quantity. As such, our results might be far from conclusive, but we can still gain important insights with proper statistical methodologies. Link to CA DMV disengagement reports Link to AV Recurrent Events Paper Hello, my name is Caleb King. I'm a developer in the DoE and reliability group at JMP. Today I figured I'd showcase what I think is a bit of an overlooked platform in the reliability suite of analysis tools, and that's the reliability growth platform. I thought I'd do that in the context of something that's become pretty popular nowadays, and that's autonomous vehicles. They're fast becoming a reality, not so much science fiction anymore. We have a lot of companies working on extensive testing of these vehicles. It's nice to test these vehicles on maybe a nice track at your lab or something like that. But nothing beats real actual road testing, which is why early in the 2010s, the state of California's Department of Motor Vehicles actually put together a testing program that allowed these companies to test their vehicles on roads within the state. Now, as part of that agreement, each company was required to submit an annual report which would detail out any type of disengagement incidents. Or heaven forbid, any crashes that happened involving their autonomous vehicles. Those had to be reported to the Department of Motor Vehicles, the DMV. Now, one benefit of the DMV being a federal institution is that these reports are actually available upon request. In fact, we can go there right now to the site and you'll see that you can at least access the most recent reports. We have the 2021 reports. They're still compiling the 2022. You could also, if you want, email. If you wanted some previous ones, I did that with a brief justification of what I was doing. They were pretty quick to respond. Now, we have different types of reports and different types of testing. We're we're focusing on testing where there is a driver in the vehicle and the driver can take over as necessary. This isn't a fully autonomous vehicle. You do have to be in the driver's seat to do this. We're using these disengagement events as a proxy for assessing the reliability of the vehicles. Obviously, we don't have access to the software in these vehicles. If you worked at those companies, you could probably have more information. We obviously don't. But they're a proxy because if you want our vehicle to be reliable, that means it needs to be operating as you intend within the environment. Any time you have to take over the AI for some reason, that could be a sign, "I t's not exactly operating as I intended." We can use it as a bit of a proxy. Again, it's not the best approximation, but it's still pretty good. Of course, I'm not the first one to think of this. This is actually an informal extension of some work I've done recently with my advisor, Yili Hong, and a bunch of other co-authors where we actually looked at this type of data from a recurrent events perspective. I'm going to do a slightly different approach here. But there is a preprint of this article available if you want to check it out that does something similar. Let me go in and describe the data you for you real quick. I'm not going to be looking at every company doing testing. There's so many out there. I'm going to focus on one, and those would be events submitted by Waymo, which was Google's self driving car project. Now they're their own subsidiary entity. These are their annual reports. Let me define what we mean by disengagement events. I'm in the driver's seat and if something's happening, I'm in autonomous mode and I need to take over and take over driving. That's a disengagement event. I disengage from autonomous mode. That could be for any reason. They, of course, need to report what that reason was. We're just using that as our proxy measure here. These annual reports are going to go all the way back to about 2015, 2014. That's when Waymo started participating in this program. The 2015 report actually contains data back to 2014. They start in the middle there. Each report essentially covers the range from December of the previous year to November of the current year. The 2016 report would contain data from December 2015 up to November of 2016. That way, they have a month to process the previous year's numbers. There are two primary sources of data we're looking at in each report. The first one is going to list each incident that occurred, when it happened that could be as detailed as day and time, or it could just be the month. Again, there's not a lot of good consistency across years. It's something we ran into. But they at least give some indication of when it happened. They might say where it happened and they can describe what happened. It could be very detailed or it could just be falling into a particular category that they give. Then the second part of data is going to list the the VIN or partial VIN of the vehicle, so the vehicle identification number. Something to identify the vehicle and how many autonomous miles that vehicle is driven that month. You might see later on when I show this data, there might be a bunch of zeros. Zero just means I either didn't drive that vehicle or I just didn't drive it in autonomous mode. In either case, I was not doing active testing of the autonomous mode of the vehicle. Now, as I mentioned earlier, there was a bit of inconsistency. Prior to 2018, when they listed the disengagement events, they actually don't give the benefit of the vehicle. We don't know what vehicle was involved. We know how many autonomous miles it drove that month, but we have no idea what vehicle was involved. Starting in 2018, that information is now available. Now we can match vehicle to the incident, which means when we do this analysis, I'm going to do it at two different levels. One is at an aggregate level where I'm going to be looking at each month all of the vehicles being tested at that time. Then looking at the incident rates overall in an aggregate measure. The second will be then I will zoom in at the vehicle level. I'll look at it by VIN. For that data, I'll only be going back to 2018. For the aggregate level, I can take all of it. Now, before we get through the analysis, actually wanted to show you some tools that JMP has available that allowed us to quickly process and accumulate this data. Again, to show you how easy it is and show off a few features in JMP. Some of them are really new, some of them have been around for a little while. Let me start by showing you one thing that helped us, and that was being able to read in data from PDFs. Prior to 2018, a lot of these data were compiled in PDFs. Afterwards, they put them in an Excel file, which made it a lot easier to just copy and paste into a JMP table. But for those PDFs, how did we handle that? Let me give you an example using data from 2017. This is actually one of the best formatted reports we see from companies. Some summaries here, some tables here and there. This in appendix A is the data I'm looking at. You can see here, this is the disengagement events. We have a cause, usually just a category here. They have the day, which is actually the month. A bit of a discrepancy there, the location and type. But this is basically just telling us how many disengagement events happen each month. Then we have a table here or a series of tables actually here at the back. This is showing us for each vehicle, in this case, it only gives partial event information. There is not a lot of information available in these early reports and then showing you how many autonomous miles were driven each month. How can we put this into JMP? Well, I could just copy and paste, but that's a bit tedious. We can do better than that. Let me come here. I'm going to go to my File, I'm going to go to Open. There's my PDF. I'm going to click Open and JMP has a PDF import wizard. Awesome. Now what it's going to do is it's going to go through and look at each page and identify whatever tables it finds there. It's going to categorize them by the page and what the table is on that page. Of course, when you save it out, you can, of course, change the name. Now, I don't want every table on every page. What I'm going to do is I'm going to go to this red triangle on this page and just say, "Ignore all the tables on this page. I don't want these." I'll say, ""Okay," I'll do the same here. It's a nice summary table, but it's not what I want. Then I start saying, " This is the data I want." Now, we're going to notice here, this is formatted pretty well. It's gotten the data I want. If I scroll to the next one, this is technically a continuation of the table from before. However, by default, JMP is going to assume that every table on each page is its own entity. What I can do to tell JMP that actually this is just a continuation is to go to the table here on the page, click the red triangle and say for the number of rows to use as header, there actually are none. This is a way to tell JMP that actually that's a continuation of the previous table. We'll check in the data here, and it looks like it did it now. I'm going to check here at the bottom and I noticed, "Oh, I missed that October data. That's okay. I'm going to do a quick little stretch there and boom, I got it." That's okay. You can manipulate the tables. I f it didn't catch something, you can stretch and manipulate the table to adjust it. You can also add tables it didn't find. In this case, I missed this. That's okay. I'm going to drag a box around it. Boom. There's a new table for you, JMP. I'm going to go in here. It's going to assume that there are some header rows. Actually, there are none. Okay, great. Now it's captured that part of the data. There's a bit of an empty cell here. That's just a formatting error because this is technically two lines, so they didn't put this at the top. It's okay. Easy fix on the back end. Now for these tables, what we notice if we go to that is it actually thinks, "Well, this is actually one table." Unfortunately, it's technically not correct because there are two tables, but it's an easy fix. I can simply go to each one and say, "It's actually not a continuation JMP. This actually has its own header." It says, "Okay," and you can do that for each of these tables. I won't do it for all of them. I'm just doing this to illustrate. What we'd have to do is we'd probably end up with a bunch of tables here where we'll have to horizontally concatenate. That's just the way they decide to format in the report. But JMP has a lot of tools to help us with concatenating and putting tables together. But you can see this is a lot easier than trying to copy and paste this into JMP, making sure that the formatting is all good. JMP is going to do a lot of that for us. Okay, another helpful tool that came out recently in JMP 2017 is, if you've probably heard of it, the JMP workflow. That was super helpful because obviously we have multiple reports over multiple years. We'd like to at least combine across all the years into two reports, one with the disengagement events, one with the mileage. What we did is we created an initial... We followed some steps to set up the table in a way that we can then concatenate them together into one table, and then we saved it into a workflow. That's what I have demonstrated here. This is a workflow builder that we put together for that. I'm going to demonstrate it using this data set. This is particular for our mileage. What we have here is a table. This represents what we would have a raw output from one of the reports. Here we, of course, have it broken down by VIN number. We've got a lot of information here. We'd like to reformat this table. First thing I'm going to do, I'm just going to walk through each step. I'm not going to show too many details in each step. You'll see what they are. It's pretty self explanatory. This first one is going to change the name of this column to vehicle. That way it matches a column in our concatenated table. I'm going to go over, I'm going to delete this total column. I don't need that. Then I'm going to do a stack across all the dates. You can see I've got that here. We conveniently called it stacked table, very informative. Now, one thing I need to do here, I put a pause here, that's the little stop sign. In. That's because I would usually need to go in and change the year. Now, something I could do right now, I couldn't really figure out a way to get a variable, say year, that you could just fill out, put the year there, and then it automatically fill it in here. That's maybe something I can go to community.jmp.com , go onto the wish list and say, "Hey, it'd be nice if I could do this." But for right now, I just put in the years. It was pretty easy to do compared to doing this multiple times. Pretty straightforward. But again, I can also highlight for you how you can actually go in and adjust the script itself. You can go in and tailor this to your needs. What this is going to do is recode these so it shows the month and the year. I'll do that real quick. There we are. The next step is going to take this. Right now, this is a category, it's a string, I want a number. That's what I do next. Now, this isn't pretty. This is just the number of seconds since some date in 1900, I believe. Obviously, that's not pretty. I'd like to show something more informative. That's what I do in the next step. Now, it shows the month and the year. I'm going to stop here. I'm not going to continue because at this point I'd have another table open. This next step would then concatenate the tables and then close off these intermediate tables. What I'm going to do is I'm going to reset stuff. I'll reset, click here. I'm going to reopen this table. I'm going to do this just so you can see how fast this goes. Here I'm going to click over here, I'm going to click Play, I'm going to click Play again. Look how fast that was. Now, imagine doing this for multiple reports. How much faster that is than repeating the same steps over and over and over again. This workflow was really helpful in this situation. Now, I'm going to close all these out because it's time to get into the analysis. Let's do that. I'm going to start with the aggregate level data. Here's my table. I compiled across all the data, all the time periods. We have the month, we have how many disengagement happened in that month. I got a column here for the cumulative total. I've got here how many autonomous models were driven. I got two columns here that I'm going to talk about in just a second. You'll have to just wait. What I'm going to do is I'm going to go in, I'm going to go to Analyze. I'm going to go under Reliability and Survival, and then I'm going to scroll all the way down until I reach Reliability Growth. I'll click that. Now we have multiple tabs here. I'm only going to focus on these first two because these last two concern if I have multiple systems. I'll revisit those when we get to the actual vehicle information. For right now, let's pretend that we're looking at the whole AV system, the artificial intelligence system in these vehicles. Think of it as one big system. There are two ways that I can assess this. One, I can do as time to event, essentially how many months until a certain event happened or days if we had that. Or I could do it via a particular time stamp. Basically, what was the time at which it occurred? I do have that type of formatted data. I have it at the month. The month is a fine timestamp. It just says in that month I had this many events happen. That's all I need to put in. I have all the data I need. I'll click OK. Now, great thing about this is before you do any analysis, you should, of course, look at your data, visualize your data. It's nice because the first thing it does is it visualizes the data for you. Let's look at it. One thing we're looking at, we're looking at cumulative events over time. What we expect is a behavior where early on we might have what I'll call a burn in type period where I have a lot of events and it's happening. I'm tweaking the system, helping fix it, helping improve it. Then ultimately, what I'd like to see is this plateau. I'd like it to increase and then flatten off. That tells me that my number of incidents is decreasing. If it goes completely flat, that's great. I have no more incidents whatsoever. I wish the were like that, it is not. But we can see patterns here in the data. Let's walk through. We have a burn in period here, early 2015, and then about mid 2016, we flatten off until about here. We see a little blip, about summer of 2016, something happens. We get a few more incidents. We level off again until we get to about here, about late spring of 2018. Something else happened because we start going up again. They're not very steep. This one's a bit longer. Then we pretty much at here, we almost flattened out. We've reached the period where we're really having no incidents happen, essentially, till the end of 2020. Then something happens in 2021 and where we've reached essentially another burn in period, something's going on. Essentially what we've got is four phases, if you will, happening in the growth of the system. Something's changed two or three times to impact the reliability. Another way to visualize this, I'll run this plot. This uses some data. I'm plotting again the cumulative total. I'm also plotting something what I call the empirical mean time between failures. It's a very simple metric to compute. It's just the inverse of the number of disengagements. It is a very ad hoc, naive way to try and estimate the mean time between incidents. But I plotted here so that you can see, you'll notice these four peaks that correspond to the bend in the curve. But there are four of them indicating these four places where something has changed in the system to affect its reliability. What we can do then is try to figure out, what are those breakpoints? One way you could do that is the reliability growth platform has a way to fit a certain model. I'll pause here to talk about the model a bit. All of these are actually the same model with slight modifications. They're all what we call a non-homogeneous poisson process. That is a fancy way to describe a counting process. I'm counting something, but the rate at which the accounts might occur per unit time is changing over time. A Poisson process just means that at a constant rate, so the rate at which incidents would occur would to be constant, that would be equivalent to seeing a straight line. It's very easy to model, but it's bad for reality because obviously we don't want the rate to stay the same. We would like it to decrease to essentially zero. That's why we have a non-homogeneous poisson process. We want it to change over time. Here we have a model where we can actually let JMP try and figure out a change point in the process. If I run it, what it's going to do is it's actually going to catch this big piece and say, "Hey, something really changed there. For most of it, it was the same thing, but after this point, it really changed." Now here it's only going to change one at a time. I have talked to the developer about, wouldn't it be nice if we could identify multiple change points? A pparently that's a bit of an open research problem, so me and him might be working together to try and figure that out. But what I did is I essentially eyeballed it and said, "I think there are certain phases. I think there's about three or four phases, and I did it empirically, which is where you get this column." I'm going to run that script. Let me show you how I did it. I come here under redo, go to relaunch, and all I did was I added the phase column here. This tells you that there are different periods where the reliability might have changed significantly, excuse me, in some way. If we think of that, we're going to look at the key metric here as the mean time between failure. We're going to see early on, so this is in months, this here is about three days, 4- 5, about a week, and this is about a day, day and a half. Early on, we have a bit of a low time. It's pretty frequent. We can also look here, I'll show you the intensity plot. That might be another thing to interpret. What we're looking for is we'd like the mean time between failures to be long. We'd like it to be a long time between incidents, ideally infinite. That means nothing ever happens, and our intensity to decrease. What we're looking here is, we get a bit of a good start. About middle of 2016, we're doing really well. In fact, we get to about a week between an incident for any vehicle. There was a bit of a blip, but we primarily get back to where we were until we get to the end of 2021, where now it's essentially about a day between incidents for any vehicle. Something big happened here at the end of 2020 with these vehicles with this software system, if you will. Again, you can see here with the intensity, you can almost do one curve and we get down to about six or seven incidents per month. Whereas here it's almost 30, essentially, once a day. We've been able to look into here and discover what's going on, at least at the aggregate level. Before we get to the vehicle level, I'm going to run one more graph that's looking at, we've got all these autonomous miles. Could it be that if I drive it more often, maybe I encounter more incidents? Could that have an effect? T here's a quick way to assess that. Just using a simple graph. We'll just plot autonomous miles versus the total disengagements. We see here for are a few number of disengagements, that might be true. The more you drive, the more you might see. But in general, long term, not really. There's really no big, strong correlation between how many autonomous miles driven, how many engagements you see. There's something else going on. T hat's actually what we found in the paper that I mentioned earlier is that the mileage impact was very minimal. Now, let's zoom in to the individual vehicle. We're not going to have all the data, even though I actually do have it here. But we're not going to have complete data for all of the vehicles. Let me break it down. I have the month, I have the vehicle identification number. Notice some of these, it's only partial. I have here what I call a VIN series. This is very empirical. I'm just taking the first four digits of the VIN. You'll see here, I'm going to scroll down a bit and we'll see. Let's see, maybe I will drag down a little bit. There we go. Some of these VINs, a lot of them actually start with the same four digits, 2C4R. I'll call them the 2C4 series. There's a bunch of vehicles that have this as their starting one. This identifies a particular fleet of vehicles, at least from an empirical view. If I scroll down, we're going to run into a different series, which I'm going to call the SADH series. This is the one that was introduced about 2021. That's when I saw the Venn numbers change to the SADH designation. Again, I have how many of miles, I have a starting month, when did that vehicle start? I'm going to use this to compute the time to events. First, I'm going to do a plot. I think this is the most informative plot you'll see for this analysis. What I've done here is I've essentially created for you a heat map. You can see I've got the heat map option select. Selected, I got for each vehicle and over time, essentially a cell, and that's just going to indicate, was I driven in autonomous mode anytime that month? I got it color coded by the series. These vertical lines correspond to the transitions between those empirical phases I mentioned earlier. What this is telling us is basically, can we identify what might have caused those transitions? Here we see an initial series of vehicles, and it looks like there wasn't a big change in what vehicles were introduced here. Maybe there was a bit of a software upgrade for this particular series that may have introduced those new incidents. Here we see that a new series was introduced, a smaller number of vehicles, maybe pilot series. Then a bunch of them introduced about that same period where we saw the other transition. Here, this seems to correspond to a new fleet of vehicles with maybe a slightly updated version of the software. Here, we see a clear distinction. Obviously, in 2021, a completely new series of vehicles was introduced. We have a bit of the old vehicles still there in the mixture, but most of them are the new vehicles. That probably explains why we got a new batch of new incidents. We got a burn in period for this new series of vehicles. This is cool because now we have a bit more explanation as to what was going on with the aggregate data, which is why it's important to have this information. Now let's break it down by VIN. I have right here script to indicate we've got a table here and it's similar to the table I have previously. Notice some of these have been excluded and this is because if for that particular vehicle, the total number of incidents was less than three, the platform is not going to be able to fit a model for you because it needs at least three incidents per vehicle. That makes sense. I have only one or two, that's not really enough information to assess the reliability. If I have three or more, now we're talking, I can do something. I also have the month since the start. I have some cumulative information there which month it started. I'm going to go ahead then and run the platform. Don't worry, I will show you how I did this. I'm going to go to the redo, relaunch. I'm going to get rid of that. That's some leftover stuff. I'm looking at one of these two last platforms. These are about multiple systems. Now we're thinking of each vehicle as its own system. The concurrent just means I'm going to run each vehicle one after the other. That's not what's happening here. The vehicles are essentially being run in parallel. Multiple vehicles are being driven at a time. Here I need a column to indicate the time to event, in this case, how many months since the start until this many events happened. I have the system ID. The one thing that's not shown here is I actually took the VIN series and used that as a by variable, which is why we have the where VIN equals so and so. There's only going to be two because the one with a little asterisks has no information about incidents. That's because that was two earlier that was prior to being able to tie the VIN to the vehicle. But they were there for completeness, I'm cancelled out of that. Now, what we're going to see here is a list of models that you could run. These first four, I'm not going to be able to do because I only have one phase. Essentially, the phase now corresponds to the VIN series. But there are two that I could run and the only difference is, do I want to run a model for all of them saying these are all part of an identical system? Makes sense. These are all vehicles, they probably run the same software. Maybe I can run a model for all of them. Or I can have a model where I fit it to each individual vehicle. Before I run those models, let's take a look at this plot. Again, start with the visualization and what we see plotted is all the vehicles. Notice here there's a bit of a shallow slope to this. Essentially there's a bit of a steep curve, but then it levels off pretty quick. This is a pretty good sign of reliability going on here. I'm going to compare them, I'm going to scroll down to the next one, the set series. Now, initially the axes here, just so you know, when you run it next time, the axes are going to only go to the complete set of data. This would actually be a smaller range. I fix them so they had the same range. You can clearly see that this is much steeper than this. Clearly, we have more incidents happening with this new series than this one. But we can do a quick model fit. I'm going to do the identical system. Again, it's a non-homogeneous poisson process. Although in this case, I'm going to ignore the estimates for right now, if you want to look at that, you can. I'm going to go straight to the mean time between failure and you'll notice that for all the months it's pretty much flat. What it's essentially done is this is just a poisson process. The rate is constant, which is good for modeling, not so good in terms of assessing it. It's just saying across this whole time for this particular series, we pretty much reached for any one vehicle, a mean time of about five months between incidents. Now, let's compare that to what we saw with the aggregate where it was about a week, that's across any vehicle. It's just saying for any vehicle, it was about a week between an incident for any vehicle. Whereas this seems to be implying it's about five months for any one vehicle. You can think of it as the running in parallel, so you can see it's staggered. Any one vehicle, it could be about five months. Again, this is an average, there's a lot of range between there. For one vehicle, it's a pretty long time. But in aggregate, they're probably staggered enough that it seems like it's about a week for any one vehicle. They can be consistent like that. But this is still pretty good. That's about five months between an incident, a disengagement event here for that series. If we run to the SADH series and do the same model, let me go here. There we see clearly increasing. I'm going to hide that. If we look at this, it says, "No," early on we probably had about two months. That's a bit of a start there. But we've dropped to less than a month, almost two to three weeks between incidents. Clearly, there's a bit more work to do on this series. Again, it was just introduced, so this is probably more of the burn in phase. If we get the 2022 data, we might start to see it level off like it did in the previous series. This actually might flip and be more of a level curve. That's about all I want to show for these platforms. I can show you some of the individual distinct systems, but there are a lot of systems here and so it's going to get crowded very quickly. There's a plot for each one. There's estimates for each one. You can look at the mean time between failure for each one. If there are particular vehicles you wanted to call out and see how they might differ, this is what you can do. You can see some increase, some decrease, but overall more or less flat. You can also look at intensity plots. If you find that more interpretable than the mean time between failure, you have other metrics that you can incorporate here. Okay, t hat's all I want to show for this platform. Now, of course, there's data I didn't include here. For example, we could break it down by cause. For some of this data, cause might be, I just need to take over because it was getting too close to the side of the the road. Or maybe the car stopped at the stop sign, did what it was supposed to, started rolling, and some other driver blew through the stop sign coming the other way. In which case, maybe that might not necessarily be a reliability hit. The car did what it was supposed to. Somebody else wasn't. It'd be interesting to break it down by that, also by location. The number of incidents, you get more when you're in the city, maybe on the highway, something like that. Real quick, we should look at the mileage impact. Again, same information, one or two incidents, sure, that might change. But overall it's going to be flat. The mileage impact on the incident rate is minimal. Of course, this is just one of many platforms available in the reliability suite. You can see there's a ton of options, very flexible for helping assess reliability. Again, that's all I have to show you. Hopefully, I've been able to demonstrate for you how well JMP can help initiate discovery and analysis. Hopefully, you discovered a lot of things about this particular company's autonomous vehicles. I hope you enjoy the rest of the conference. Thank you.
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Generating and Evaluating Lot Acceptance Sampling Plans using JMP® (2023-EU-30MP-1315)
Saturday, March 4, 2023
In a regulated environment, systems are put in place to ensure product safety, efficacy, and quality. Even though ‘You cannot inspect quality into a product.’, as Harold F. Dodge said, the CFR - Code of Federal Regulations, 21CFR820.250(b) states, “Sampling plans, when used, shall be written and based on a valid statistical rationale.” Lot acceptance sampling plans (LASP) provide the statistical rationale and have been used in many industries. Practitioners, however, usually rely on the tables in ANSI/ASQ Z1.4 (attributes) and Z1.9 (variables). Since the operating characteristic (OC) curve is the best way to evaluate and compare lot acceptance sampling plans, an add-in was needed to facilitate this process. The evolution of appropriate sampling plans within the biotech or medical device industries that balance the customer design requirements and technology opportunities lead us to develop an interactive JMP Add-in for designing and evaluating valid lot acceptance sampling plans for attributes and variables. During this highly interactive session using JMP, we will demonstrate how to use the add-in to efficiently design and evaluate lot acceptance sampling plans by showcasing its flexibility and ease of use. Live from 100 SAS Campus Drive, your one and only SAS world headquarters in Cary, North Carolina. It's The Sampling Plan Show starring Stan Koprowski. Now here, he is the host of The Sampling Plan Show , Stan Koprowski. Thank you. Thank you very much. You're too kind. Thank you. We love you. No, that's enough. Please. All right, we have a great show for you today. Before we get started, just a little background here. I wasn't very good at statistics. In fact, I got a paper cut from my statistics homework. What are the odds? Enough of that. Today we're going to talk about acceptance sampling and sampling plans. We're going to learn about the OC curve and what it is. I'll be honest, this is one of my all time favorite ANSI standards. We're going to hear about the ANSI standards. We'll make some predictions for the big sampling plan, and then finally, we'll show you some fantastic highlights using the JMP sampling plan added with one of my all time favorite industrial statisticians, Dr. José Ramirez. I'm glad to be here. Proud of this show. I think it's going to be fun. S orry to hear about statistics being hazardous to your health with a paper cut. Man. It was a rough journey there, but we'll get through it. As you see, here's the title of our talk, and then I will play some other slides here for you. Your book, I like this book. We use this a lot in the division. The Statistical Quality Control book: The JMP Companion . This is the companion book to Doug Montgomery's book. I think you're going to mention Doug's book later in the talk as well. That's true. Then I have your other book up here too, Analyzing and Interpreting Continuous Data Using JMP . I know you've been a long time user of JMP, probably back since JMP two. I think you were probably one of the first support cases that came into tech support with their own director of customer enablement, Jeff Perkinson. Jeff, as we all know, started out in tech support and I'll have to look back through the cases. But what I understand, you were a long time user of JMP. Welcome to our show. I'm glad you could be with us today. Super excited to have you. We're going to talk about some options here for the folks to call in. If you want to call into the show or message, you can message us at JMP L ot Plan. The phone number, if you have a rotary dial phone, you can call us up there, or if you want to reach us on Instagram at JMP Lot Acceptance Sampling Plan. Let's go ahead and see. I think I do hear a call again. Wait a minute. Let me see who that is. Yes, this is Dr. Julian Parris calling in. I'm the Director of JMP User Acquisition and I have a question for Dr. Ramirez. Go ahead, Julian. Dr. Ramirez, can you explain the difference between lot acceptance sampling plans and variables acceptance sampling plans? I don't really believe that was Julian. That was probably Julio from down by the school yard. Julio, do you have a question? Yes, he did. He was asking if you could give us a little introduction to sampling plans. Sampling, okay . He's doing some sampling by the schoolyard. I wonder what he's doing there. Julio, since you're in a school, you're by the schoolyard, let's go back to the dictionary and look at sampling, what the dictionary says. There are a few definitions here in the dictionary, and one thing I like about the first one it talks about a suitable sample. For statisticians that has the meaning, part of that is there, the sample has to be representative. For those of you familiar with stats, the other piece that we also include in a definition of suitable sample is random. But what you're talking about, about these sampling plans is the second definition, and they got it right because a sampling plan is essentially deciding on a small portion of a lot or a population. The population can be either infinite or it can be a finite number like 10,000 items. We want to take a small sample from that so we can do some inspection. By inspection, we mean that we are going to decide the fate of that population or the fate of that lot. Okay, I understand a little bit of what you're saying there. You're going to take a sample from a population and then what's the difference between the sample types? It looks like you were talking here about an inspection. Is it always an inspection or are there other types of sampling that you can do? Well, in general, when people think about lot acceptance sampling plans, there's some type of inspection, some type of checking that goes on. T he way we're going to do this sampling is we're going to apply some statistical principles [crosstalk 00:06:22]. Oh, gosh, that's scary. But actually that's what the agencies want. For example, if you look at some of the documents from the FDA, if you're required to do some type of sampling, they want you to do it in a statistical way. How are we going to do that? That's part of this show that we're having. But in the old days, and we're going to talk about those old days, people used standards, and those are the standards that are used right now. The ASQ/ANSI Z1.4 and Z 1.9. That sounds even scarier, but I'm a bit confused. I thought when I was doing my research before I brought you on as a guest, that there were some military standards. I thought the sampling plans were based on military standards and now you just told me there's some other standards in play here. W hat's the story with this? Well, yeah, that's true. These sampling plans have a long history. They go back to probably the 1930s, 1940s, and there were two military standards, the 105 E, which is the one that corresponds to the Z 1.4, and that's what you're doing using a technical term, sampling by attribute, and then there is the military standard, 414, which is the Z 1.9 that corresponds to sampling by variables. You can think of discrete versus continuous. What happens is that those standards were taken over now by the American Society for Quality, that's ASQ, and the American National Standard Institute, the ANSI, and they rebranded those, and now they're called Z 1.4 and Z 1.9. But they're still the same standard, the same table that people use to generate sampling plan. L et's talk about how we do that. The way to understand this is that every type of lot acceptance sampling plan, and that is the L-A-S-P or LASP, has components and risks. T he components of the plan essentially is how many samples do I need to take from a population of 10,000 or infinity that I'm going to test? I'm going to do some inspection, and I want to know if in that sample, there is a pre- specified percent defective. We're going to define quality as a percent defective in the population. What we're doing is taking a sample to see if that sample contains that or less. In order to determine if we're going to pass or fail that lot, we also relied on the acceptance number, which tells us how many defective parts out of the sample we can accept in the plan. Anything that is greater than that, it's going to say we're going to reject or fail the lot. That's why, as I said before, we're determining the fate of the lot with this [inaudible 00:10:03] . Here we go. I had my fate a long time ago with that paper cut. I'm a little anxious here. What kind of fate are we talking about? Yeah, we're going to decide if we are going to pass the lot and make it available for whatever purpose that lot is being manufactured, or we're going to put it in quarantine [inaudible 00:10:31] and maybe do some more inspections or trying to understand why the fraction defective is larger than the prespecified one. You mentioned that there's some risk involved. What kind of risk? Is it out of my control or is it within my control? Whose risk is this? Yeah, there's risk, and that's the beauty about my profession. In statistics, you don't have to be certain about anything. I can 95% confident. I can even go up to 99% confident, I don't have to be certain. Again, for those of you familiar with statistics, you know what we're talking about. There's the chance that there's a perfectly good lot that we're going to reject because, again, we're taking a small sample, so we're not sampling the whole population. There's a risk associated that this sample may determine that a good lot is not good or there's a chance that a bad lot may be released. Y ou can think in terms of false positive. Y ou think in terms of a medical test. That's similar to like if you were to take the COVID-19 test. If you actually had it and it came back negative , is that a false positive? Yeah, it's like that, a false positive. We may have a false positive that means this lot is bad, but the sampling plan that it was good. Or vice versa, we have a good lot and we're going to reject it because the sampling [crosstalk 00:12:19] . That's why it's important to use a statistical principle in designing these sampling plans. That's part of why people use standards. Those were derived in a way that when you select a plan from those standards, these risks are going to be balanced. That's what we talk about the generation of the lot acceptance sampling plan. We have these two risks. Again, we can say that a good lot is going to be rejected and there's also a chance that a bad lot may be released like the false positive. We're going to assign some probabilities to this risk. One thing we want to make sure is that if it's good, we want to accept that most of the time. How do we define that? Well, standard practice is used 95%. A gain that sounds like 95% confidence when you use some type of statistical test. What we're saying is we're going to pre define a fraction defective for that lot and we are going to select a plan that guarantees that or almost guarantees that 95% of the time, a good lot is going to be accepted. A good lot is going to pass. On the other hand, we're going to also say that we're going to have a high chance of rejecting a bad lot. Or if you flip that, you can say there's a small chance of passing a bad lot. 90% here transforms itself to a 10%. We're saying there's a 95% of accepting a good lot, but only a 10% chance of accepting a bad lot. Those are standard numbers in sampling plans, in the standards and the way people use those sampling plans. You were talking about the user. The user has the option of changing those numbers. Rather than use 95%, we can use 99%. Rather than using 10%, we can use 5%. However, if we get too greedy, then as some of you may know, the sample size is going to increase and that's part of that balance. We want to find a sample size that is small enough that it's going to guarantee these probabilities. Should we do some examples? Are there things we could do to explain some of the balance that you're talking about between these two competing risks? Yes. It sounds like one might be on the consumer side, and maybe one of the risks is on the producer's side. Exactly. The first chance of ... If you look at the 95% chance, that means there's a 5% chance that a good lot is going to be rejected. That's on the producer side. Because as a producer, you don't want your good lot to be rejected. The loss of material or good product. On the other hand, the 10% chance of accepting a bad lot, that's on the consumers because we don't want the consumers to receive something that is bad. Now, in order to do this, there's this tool that we use in sampling plans which is called the operating characteristic curve or OC curve. Some people may be familiar with that. Those curves, I'm going to show you some examples, are the ones that are used to find that balance between this risk and probability. Risk and probability. What is an OC curve? An OC curve is essentially a plot that shows you the probability of accepting a lot as a function of that fraction defective in the population. You look at these two curves, the blue line here on the Y- axis, we have the probability of accepting the lot, and the X- axis, we have the proportion defective in the population. As you can see, when the proportion defective is very small, there's a high probability of accepting the lot. A s soon as that probability starts increasing, then the probability of accepting that lot decreases, and that's the shape that we want to see in an OC curve. Now, remember we have two probabilities and two risks. We want a 95% chance of accepting a good lot and a 10% chance of accepting a bad lot. Now, the definition of good and bad is in terms of that proportion defective in the population. We as users of sampling plans, we have to define what is a good fraction defective. Of course, ideally, the good fraction defective should be zero, but that will throw a [inaudible 00:17:22] in the math. If you know that zero divide by zero, you get infinity, meaning you have to sample everything if you want perfection. What we do is define a small number, and that is called the AQL, the acceptable quality level. On the other side, we define the RQL or rejectable quality level. Granted, there are many terms that people use. Sometimes you may see the LTPD instead of RQL. That's the lot tolerance percent defective, the maximum percent defective that you can accept in your population. With the probabilities and those fraction defectives, you define two points in this curve. H ere, the AQL is 1.8%, and we have a 95% probability of accepting that. What that means is, as long as the fraction defective in the lot is less than 2%, let's say, then there's a high chance of accepting that lot. But as long as it goes higher, like 2%, then we're going to accept that lot very infrequently. These two points are the ones that you look at in the OC curve, and those are the ones that are going to determine the sample size and the acceptance criteria. Got it. I think it will help Julio if we run some example. I think Julio is texting and he said that would be great if we could do an example. Okay, let's do that. Why did you develop or why did we produce a JMP add-in? Yes, Julio, what we're going to do is we're going to show you an app to do this. People sometimes ask us, "Why did you do this? Why do you spend a lot of time for writing code and package it in an add- in?" Well, to tell you the truth, [crosstalk 00:19:30] . That is really tiny. I'm going to have to get a new set of readers or a giant magnifying glass. Is that really how people do this? Now it may seem weird, but still, I believe in some industry, people may use a standard. Granted, it may not be the old book that they use, maybe a pdf, but sometimes they still use this. But these are very tedious and they're discreet in the sense that they're just approximations to the plans. T here's a process to do that. Of course, there are multiple tables that you have to go through in order to find the appropriate sample size. W hat we wanted to do is make our life easier, actually, because to be truthful here, we use sampling plans. We're also tired of using these tables. So we wanted to automate the generation of the LASPs. Okay, got it. What else do we need to know before we do some examples? This is JMP, which is one of the greatest pieces of software out there for doing statistics and getting insights out of your data. A nother thing that we did is that not only we're automating the generation of the plans, making life easier, but we're using all the visualization tools in JMP, like the profilers, to understand these OC curves. Remember, we just showed you that the way we determine or generate the plans is via an OC curve. The OC curve is also very important to evaluate plan. How do we know? If someone gives us a sampling plan, how do we know if that's good? T hat's part of this. Let's look at an example. T his comes from Professor Montgomery's book. A gain, here a shameless PR for us. We wrote a companion book. You saw that book at the beginning for this. It's called JMP Base . I f you look at this figure, this is in chapter 15, P rofessor Montgomery also shows another approximation. It's another way of generating sampling plan, which is using a nomo graph. A nomo graph is this figure that you see here. Here, you have to figure out what your AQL and RQL are, the probabilities, and that you have to go in there and approximate that. Here, they give you an example where they say the acceptable quality level is 2% or 0.02 , the rejectable quality level is 8% or 0.08 . The probability of accepting a lot that has an AQL of 2% of less is 0. 95. As we say, that's high. T he probability of accepting a lot that has RQL of 8% or more is only 10%. A gain, these 0. 95 and 0.1 are our standard value in industry. Y ou notice this diagram here, they put the 0.02, they draw a line here to intersect the 0. 95. The 0.08 intersects at that, and then you intersect those two lines and you guess at that. Okay, that's a 93 plan. Meaning, out of an infinite population, you take 90 samples, you inspect them all, and you can accept up to three defectives. If you see more than three defectives, you reject the lot. If you see three or less, then you accept the lot. T hat's sampling plan. A little cumbersome too . Yeah, it looks a little difficult to line up exactly. I hope we could get away from doing that with the add-in. Well, let's show Julio how we can do that with JMP. Okay, sounds like a good idea. All right, Julio, let's jump over to some examples here. H ere is JMP and to get to the add-in, you just go to your add-in, JMP Sampling Plans, and let's look at attribute sampling plans. T hen this particular one, we're going to do just a single lot acceptance sampling plan. W e have the menu. After we make that choice, it comes up and it gives us three options. We can evaluate an attributes plan, we can generate or create an attributes plan, or we can compare plans. The add-in gives us the ability to compare up to five different plans. There's an option to keep the dialogue open in case you want to look at more than one plan. L et's go ahead and generate that plan that you were just sharing here from Dr. Montgomery's book. I'll give you the numbers. Let's see. All right. This is the interface. [inaudible 00:24:51] AQL. Right. Y ou have the couple of different sections in the interface. You get to put in your quality levels, your probabilities, and then you have the optional area about the type of lot sampling you're going to do. T hat lot sampling is based on a distribution. S o we could either do a hypergeometric distribution or a binomial distribution. Lets put Montgomery's numbers there. The AQL in Professor Montgomery's book is 0.02 or 2%. The RQL he has is 0.08. All right. Oh, they're pre- populated. Yes, those are the standard values, 0.95. The standard values. So we keep the default there. I think you told me there was no theoretical lot size here. So we're going to do type B. Actually, in Montgomery's book, it says that he's using a binomial nomograph, so it's meaning he's using the binomial distribution. So yes, that's the right choice. Then all we have to do is hit okay, and then what happens? JMP gives us this curve here and an output window and a report. T here's three sections to the report. You get a little at the top of summary, you get a summary of the plan. I t shows you your input parameters and then what the plan generated as a sample size and acceptance number. T hen it gives you some information on how to interpret the plan. That was helpful. O ut of the plan recommendation here, of the 98 samples, it says you can accept the lot here as long as the number of defectives is less than or equal to four out of that 98 sampled. O therwise, you reject it if it's greater than that. T hen it tells you some additional information there. I f we look at that OC curve, I think this is what you were showing us earlier. Yes. So we have a probability at 95% and the quality level of 2%. But if I recall, you told me in the literature that this was a plan of 90 and 3. So I'm confused again here or, should I say, Julio texted me and said he's confused. How is it different here? Or why is this different? We're getting 98 and 4. Professor Montgomery, in his book, he's showing the nomo graph and he's getting 90 and 3. Let me go back there just 1 second. R emember, what we're using is this graph here where you have all these approximations. You have a line that goes 200, 300, you go between 70 and 100. Got it. There's not really a 93 or 94, anything like that. A s I say, you had to approximate these. T his nomog raph is just an approximation. What we're actually trying to do is solve these equations for those of you mathematically inclined. The one minus alpha is the 0. 95, the beta is the 0.10 , p1 is the AQL and p2 is the RQL. I f we put all those four things in here and we solve these equations to try to find the minimum n that satisfied those four things. W hen you do that, actually, software does that for us, the code that we wrote, we get the 98 and 4. I get that. The moral of the story here is that the nomograph gives approximate lot acceptance sampling plan again, because it's just an approximation game. T his is again one of the advantages of using the add-in, because you get- Sampling plan add-in, of course. -more exact sampling plan. Y ou show that we can evaluate a sampling plan. L et's do that. Why don't we evaluate this plan, the 93? Show us ho w to do that. Since I left that window open, we don't have to go back to the menu again. L et's evaluate a plan now. W e have in that interface, again, we're going to put in our previous 0.02, 2%, and I think he told me the RQL was 8%. Yes. This time, though, instead of a sample size of 20, we're going to do 90 and evaluate three. Again, it's a binomial. Here, you're entering the four quantities that we talked about in the generation, but you also enter the actual sampling plan that you want to evaluate. Exactly. Now when I say, okay, it's going to look... Sorry about that. I'll j ust redo that quick. [inaudible 00:30:31], no? Yes, sorry. I want to evaluate that plan, you get 90 and 3. Three, yes. When I rerun that, we get the exact same style of report, but the information is slightly different, I see here. I noticed also down in this table, there's some color coding and direction of the arrows. Yeah, I see some red there. I see some red, yes. That's an issue. Is this an indication that the specified quality level is better? Actually, no. I think that the reason that it's red is because what happens here is what this is telling us. If I use a sample size 90 with an acceptance number of three, you can see the associated probability of acceptance is 0. 89. Remember, we wanted that to be 0. 95. So it's actually lower. It's lower and that's why that is red. T his add-in is giving you a signal that your probability of acceptance is less than the one you specify and that's an issue. T hat's why the 98 and 4 is a better plan. A lso you can see at the bottom that the probability of acceptance for defective lot is 6% rather than 10%. I n that case, it's blue because it's better, that probability is better. Y ou're going to have a smaller consumer's risk. You can see that the n is 6.47 versus the 10. 67. T hat is what's happening. That producers risk is really just the difference between that one minus the associated probability of acceptance there? Exactly. Got it. Exactly. T he producer's risk, we want it to be 5%, and in this case, it's 10%. No w this is something that I haven't seen in any other software. Normally, what you see is the plot on the lot. You get that for the associated AQL and all that, and they assume that the probabilities are the ones that you specify, but they're not. W hat the add-in does too, is it flips things around and says, okay, if rather than fixing the AQL and RQL, I fix the probabilities, I want to be 95 because that's what it is, I'm neurotic that way. I want it to be 95 and 0.1. Then what are the corresponding AQL and RQL for that? T hat's what that's telling me. Okay. I wasn't really just seeing double. It's really a different calculation on the right hand side. Got it. What that says is if the probability of acceptance peaks at 0. 95, then the AQL is not 2% but 1.53%. Okay, got it. It has to be way less than that. So it's a... Okay, go ahead. A lso at 0.1, then it's not 8% but 7%. Okay. 7.3%, roughly. That's why both cases, they are blue. They are blue. A gain, just very quickly here to show this. In summary, for this one is that, again, the nomogram gives an approximate lot acceptance sampling plan. The 90 and 3 plan shows you that we're not hitting that. Are you sharing? Yes, I'm sharing. Okay. Hopefully, people can see that. Our producers risk is now 10% versus 5%. But there is one more thing that you had there, which is compare. I'm curious, can we use that? I'll tell you tell can tell you what I want to do. I want to use the add-in to compare the planning Professor Montgomery's book of '93 with a plan that the add-in gave us, which is 98, 4 All right. W e did mention that we can compare up to five different plans. Y ou want me to compare the two plans that we just created. All right. Again, that 8% RQL. In this case, we had a 90 and an acceptance of three. Yeah. Then I think you told me it was 98 and 4. That's what they do. I didn't tell you. That's what the add-in gave us. Yeah. Wow, that, that is pretty slick. A gain, if you wanted to do more than two, you would just check the additional rows and then the add-in will calculate up to five different comparisons here. I'm going to go ahead and click OKay. N ow it looks a little bit different, Jose, here. Now I'm seeing two OC curves. I'm getting a comparison of both of my OC curves on the same plot I see here. Exactly. H ere the blue one is the plan 93 and he red line is- T he blue is the 93. Yeah, exactly. I see that. The red line is the 98, 4 . Y ou can see that the red line or curve, is on top of the blue curve. T hat's what you want to see. You want the curve to be on top, literally. T his shows you that you have higher probabilities of acceptance according to the parameters, the IAQL and RQL that we prespecified with the plan 98, 4 than with the plan 93. T his is very helpful because you may be in situations where you may get an approximate plan from a book or someone may suggest, "Hey, why don't you use this plan?" With this one, you can compare them all and see which one is better. I t's easier to negotiate the sample size using these tools than just getting into an argument and say, "No, we should use 93 because that's in the book or something like that. "I think that's a great feature to be able to compare, and that way you move the discussion into some actual information and rather than subjective, individuals can now compare directly. Ag ain, if you want the reference lines, it didn't make sense to show all the reference lines across five different curves. W hat we've done is we could just display a single set of reference lines by toggling the filter, and it will update the graphs for you as you toggle between them. That way, if you do want to see that individually, you could do that, and then you're just focused in on the table and the graphs for that particular set of observations. That's, I think, a nice feature, Julio. It is. All right. It's time for one more. [inaudible 00:38:32]. I think that Julio is probably getting tired there. He is very exhausted. Julio, there's some other things that the add-in can do. So maybe it should be a follow up to this. Maybe we could do another session for Discovery Summit Japan or Discovery Summit China. We'll continue showing. [inaudible 00:39:05]. I think I'm getting breaking news coming across here. Just into the news center here of the show. A nyone, it looks like, can get that sampling plan add-in. If you just go to the JMP user community and search for the sampling plan add-in, you could download it yourself. And that's free. No? That's free? Absolutely free. We would never charge for that on the community. G o ahead and download that. If you have feedback or you run into issues, feel free to message me, and we'll get those defects entered and get a corrected version out there as soon as we can. Other things just before we wrap up here, I just want to say thank you to Jose. Thank you for joining us on the JMP sampling plan show today. It was great to have you here. Really great. T hank you again.
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Disallowed Combinations and Operating Region Optimization for CQAs with the JMP® 17 Profiler (2023-EU-30MP-1305)
Saturday, March 4, 2023
The prediction profiler in JMP® is a powerful tool for visualizing and optimizing models from designed experiments. This presentation will focus on new features in the prediction profiler for exploring and optimizing models with known constraints and for determining factor ranges that assure quality as defined by the specifications associated with Critical Quality Attributes (CQA), thereby solving a fundamental Quality by Design (QbD) problem. While previous versions of JMP were able to create designs that respected disallowed combination constraints and combinations of factors that are known in advance to be physically impossible or undesirable, the model exploration and optimization in the profiler in the last step were unable to obey these constraints. We will demonstrate how the profiler, since JMP® 16, handles these complex design constraints automatically when exploring the model and performing optimization. We will also demonstrate how the Design Space Profiler, new in JMP® 17, finds subregions of the design space that maximize the probability of maintaining product quality for the CQA specifications while maintaining maximum flexibility. These two capabilities make the prediction profiler indispensable for a high-quality product and process innovation. Hello. My name is Laura Lancaster. I am a statistical developer in the JMP group. Today, I'm here to talk to you about dissolved combinations and operating region optimization for critical quality attributes with the JMP 17 profiler. Everything we're going to talk about today has to do with the Prediction Profiler, and I hope that everyone is familiar with it. It's a wonderful tool. But if you're not, the Prediction Profiler is a tool in JMP that's great for interactively exploring, visualizing, and optimizing the models that you create in JMP. Specifically, we're going to talk about two recent new features that were added to the Prediction Profiler. The first is the ability to explore and optimize the models that you've created in DOE in JMP that have known disallowed combination strength. The second is the ability to determine an optimal operating region for your manufacturing processes that ensure both quality and maximum production flexibility. Let's go ahead and get started talking about exploring and optimizing models from designed experiments with this all combination constraints. I t often happens that when you're designing experiments, it's not possible or it's not desirable for various reasons to be able to experiment over the usual entire rectangular design region. When that happens, you need to be able to apply constraints to your design region before you create the design and certainly before you run the design. Thankfully, ever since JMP 6, which has been a long time, the custom design platform has been able to create design experiments with constrained design regions. Since then, constraint support has also been added to fast, flexible filling designs and covering array designs. Now, what types of constraints are available in JMP's DOE platforms? The first type of constraint is the simpler of the two. It's linear constraints on continuous and mixture factors. H ere's a picture where we have two linear inequality constraints that are shown in the gray shaded region. Then the design, you can see stays out of the disallowed linear constrained region. The next type of constraint is called a disallowed combination constraint. It's a more general and can be more complicated type of constraint. It can consist of continuous, discrete, numeric and or categorical factors. What it is is a constraint that's a JSL Boolean expression that evaluates to true for factor combinations that are not in your design region. Here's an example. We have a two- factor design where X1 L1 and X2 L3 cannot be in the design. They're disallowed and they're written as a JSL Boolean expression, which you can see right here. N otice that this design is created and stays out of this disallowed region. Now, originally, all of these disallowed combination constraints had to be entered as JSL like this. But then in JMP 12, a disallowed combination filter was added that made it easier to create these JSL expressions if you have fairly easy disallowed combinations such as individual factor ranges combined with and/ or expressions. We'll look at an example of this shortly. Now, what about the Prediction Profiler with constrained regions? Why is it important for the Prediction Profiler to be able to obey constraints when you have models with constraints? Well, if the profiler ignores constraints, then it's possible that the user could navigate to predictions that are not feasible and they may not realize it. So you could end up in an area that's not possible, not desirable, and you certainly haven't tested there. It's an extrapolation, so this is bad. Then probably even worse, if you want to optimize your model, you could end up with an infeasible optimal solution. If that happens, the user would have to either try to manually find a feasible optimal solution, which could be really hard or even impossible, or they would have to use another tool. What were the challenges with getting the Prediction Profiler to obey constraints? Why did it take so much longer to get these constraints in the profiler versus DOE? Well, the main reason had to do with the constrained optimization. The desirability function is a nonlinear function. That means that our optimization has a nonlinear objective function and possibly both continuous and categorical factor variables that could be involved in constraints. This is known as a mixed integer nonlinear programming problem, and it's an extremely difficult type of optimization problem unless you know something favorable about your objective function or your constrained region. It's just very, very hard. But good news, the Prediction Profiler now works with all the same constraints as the DOE platforms. Turns out that the Prediction Profiler has actually obeyed linear constraints on continuous variables all the way back to JMP 8, j ust a couple of releases after they were added in JMP 6. We were able to do this sooner because these constraints, these linear constraints on continuous variables have really nice properties. B ecause of that, we were able to implement a wolf- reduced gradient variant algorithm. That algorithm does a really, really good job of finding the global optimum, especially if you don't have categorical variables. In that case, you should find the global optimum. Now, since JMP 16, the Prediction Profiler now also obeys disallowed combination constraints on both continuous and categorical variables. Now, this was a lot harder because these constraints are very general. You could put absolutely anything inside that JSL Boolean expression. So we cannot assume anything favorable about our constrained region in these cases. Thus, we had to implement a genetic heuristic algorithm, which is a very general type of algorithm for the constrained optimization. B ecause of this, we can't guarantee a global optimum solution. But you should find a solution that's very close to global optimum, if not the global optimum. Let's go ahead and start looking at some examples. First, we're going to look at a chemical reaction experiment. This experiment has one response, and the goal is to maximize yield. We have three factors. Two of them are continuous, time and temperature, and catalyst is categorical. We have two constraints. When catalyst B is used, temperature must be above 400. When catalyst C is used, temperature must be below 650. W e used Custom DOE to create a response surface design with dis allowed combinations. Because these are fairly simple constraints, we were able to use the disallowed combinations filter. You can see here that when I set catalyst to B, temperature cannot be below 400. This is my first disallowed region. Also if catalyst is C, the temperature cannot be above 650. Then once we created the design, you can see that the design points stay out of the constrained regions that are gray here. These are the dissolved combinations regions. Then we ran the experiment and we used Fit Least Squares to fit a response surface model to the data. Now, I want to show you how you would use the Prediction Profiler to explore the model and find the maximum yield. I'm going to go get out of PowerPoint and go to JMP really quickly. Here is the data table from the chemical reaction experiment that we created with JMPs custom design platform. We've already run it and entered all the results. The important thing I want to point out is that Custom DOE added this data table script called Disallowed Combinations. When you open it up, you see it's got the JSL Boolean expression of my disallowed combinations. And this is what the Prediction Profiler reads in, and that's how it knows about my disallowed combinations constraints. I've already saved my response surface model, and I'm going to run it and go down to the profiler. Because I have that disallowed combinations constraint saved in the table, it's able to read those in and the profiler can obey the constraints. I f I set catalyst to B, notice that I cannot get to a temperature 400 or below. If I set catalyst to C, I cannot get to a temperature 650 or above because those are disallowed regions. Also, when I maximize yield, I end up with a solution that is feasible, it's not in a disallowed region. Now, what would have happened in a version of JMP prior to JMP 16? Well, we can see what would have happened by looking at the exact same data table without the disallowed combination script. I'm going to run the same exact model and go to the profiler. Now this time, the profiler doesn't know about my constraints. So when I set catalyst to B, I can go down into a disallowed region down to 350. Catalyst C, I can wander up into another disallowed region, temperatures above 650. W hen I do the optimization, I do end up with an infeasible solution. I'm in the disallowed region where catalyst is C and temperature is 750. I would be forced to have to try to manually find a feasible solution that's not in a disallowed region. But thankfully, that's been solved since JMP 16. Let's go clean up and let's go to another example. Okay, the next example we're going to look at is a tablet production experiment. The goal of this experiment is to maximize dissolution. We have five factors. Four are continuous and one is categorical. We have two constraints. The first constraint is that when screen size is 3, mill time has to be below 16, and my spray rate and coating viscosity follow this nonlinear constraint. I used Custom DOE next to create a response surface design with disallowed combinations using these two constraints. Because this is a complicated constraint, we could not use the disallowed combinations filter, so we had to enter it as a script, which is not hard to do. Here's where I've entered that nonlinear constraint as a script. Notice I've flipped the inequality to show what's disallowed instead of what should be allowed. T hen I've also added screen size equals 3 and mill time greater than 16 as the other disallowed region. Now, we can see by looking at two different slices of my design. This first graph is spray rate versus coating viscosity. I can see that all the design points stay out of the disallowed region set by this nonlinear constraint. W hen I look at screen size versus mill time, when screen size is 3, m ill time cannot be above 16. Then we ran the experiment, and we used Fit Least Squares to fit a response surface model to the data. N ow we're going to use Prediction Profiler to explore the model and find the maximum dissolution. I'm going to go back to JMP. This is the tablet production experiment that was produced by JMP's Custom DOE platform. N otice that once again, it has saved the disallowed combinations data table script to the table. I'm going to look at that. You see that it's the JSL Boolean expression of my dis allowed combinations, and this is what the profiler will read in. I've saved the response surface model to the table. When we go to the profiler to explore the model, you can see that it obeys my disallowed combinations constraint. When screen size is 3, mill time cannot be above 16. Also, spray rate and coating viscosity obey that nonlinear inequality constraint. When I maximize the solution, I end up with an optimal solution that's feasible and notice that it's actually on the constraint boundary. T hat tells me that if I had not been recognizing the constraints, I almost certainly would have ended up with an optimal solution that wasn't feasible, and I would have had to try to manually find it, which would have been very difficult, if not impossible. All right . Let's move on to the next topic. Here we go. Back to PowerPoint. Okay . Our next topic is operating region optimization for critical quality attributes. This is where I'm going to introduce the new Design Space Profiler that's new to JMP 17. What do we mean by design space when we're talking about the Design Space Profiler? Well, this is an important concept that's used in pharmaceutical development that identifies the optimal operating region that gives maximal flexibility of your production while still assuring quality. This concept was introduced by the FDA and the International Conference on Harmonization when those agencies decided to adopt Quality by Design principles for development, manufacturing, and regulation of drugs. W hen they did that, they put out some really important guideline documents, ICH Q8-Q12, that most drug companies follow. Specifically, we want to look at ICH Q8 ( R2) , which covers design space. It defines design space as the multidimensional combination and interaction of material attributes and process parameters that have been demonstrated to provide assurance of quality. Now, there are a number of steps that need to be taken to determine design space for a product, and several of them need to be done before you can get to the Design Space Profiler and JMP. One of the first things that you need to do is you need to determine what your critical quality attributes are and what the appropriate spec limits are to maintain quality. We'll refer to these critical quality attributes as CQAS. The ICH document defines a critical quality attribute as a physical, chemical, biological, or microbiological property or characteristic that should be within an appropriate limit, range, or distribution to ensure the desired product quality. This is the important first step. Next, we want to use designed experiments to determine what are our critical manufacturing process parameters that affect those critical quality attributes. We'll refer to these as CPPs, critical process parameter, because ICH Q8 defines a critical process parameter as a process parameter whose variability has an impact on a critical quality attribute and therefore should be monitored or controlled to ensure the process produces the desired quality. Then, once you've determined your CQAs and your CPPs, then you want to find a really good prediction model for your CQAs in terms of your critical process parameters. Once you've done all of that, you can use the Design Space Profiler to determine a good design space for your product. Let's talk a little more specifically about the Design Space Profiler and JMP. The goal of the Design Space Profiler is to determine a good design space by trying to find the largest hyper rectangle that fits into the acceptable region that's defined by your critical quality attribute specifications applied to that prediction model that you found. Once you found that hyper rectangle, it will give the lower and upper limits of your critical process parameters that determine a good design space. The problem is that that acceptable region is usually non linear, and finding the largest hyper rectangle in a non linear region is a very, very difficult mathematical problem. Because of that, we wonder how does the Design Space Profiler actually determine Design Space then? Well, instead of trying to find the largest hyper rectangle mathematically, we use a simulated approach. What it does is it generates thousands of uniformly distributed points throughout the space defined by your initial CPP limits. Then it uses that prediction model that you found to simulate responses for your CQAs. Note, because your prediction model is not without error, you should always add response error to your simulations. Once you've got your simulated set, it calculates an in-spec portion, accounting the total number of points in that set that are in-spec for all your CQAs from all the points that are within the current CPP factor limits. This is easiest to see by actually looking at an example and going to JMP and looking at the Design Space Profiler. That's what we're going to do next. We're going to look at an example of a pain cream study. The goal of this study was to repurpose a habit- forming oral opioid drug into a cream that provides the same relief as the oral drug. T he first thing that we needed to do was determine our critical quality attributes for this drug. We determined that there were three of them entrapment efficiency, vesicle size, and in- vitro release. We also needed to determine what are the spec limits that assure quality. That's what these numbers are. Next, we ran experiments to determine which of our manufacturing process factors affect these critical quality attributes. It turns out there were three of them. They are emulsifier, lipid, and lecithin, and these are the initial factor limits for these CPPs. Next, we used custom design and Fit Least Squares to find response surface models for our three critical quality attributes in terms of our three critical process parameters. Once we did all of that, now we're able to go to the Design Space Profiler and JMP to determine a design space for this pain cream. Let's go back to JMP. I'm going to open up my pain cream study. T his was my response surface model design created in JMP's DOE platform. I've got my design in terms of my three critical process parameters here, and these are my three critical quality attribute responses here. The important thing I want to point out is that for each of these critical quality attribute responses, I've saved spec limits as column properties. T hat is because the Design Space Profiler has to know what the spec limits are for your critical quality attributes. So if you don't enter them as column properties, you'll be prompted to enter them once you launch the Design Space Profiler, unless you've added them here. I've already saved my response surface models as a script. I'm going to run that script. It launches Fit Least Squares, and I have it set up to automatically show the Prediction Profiler. This is the same Prediction Profiler that you're probably used to seeing. I have my three responses, my critical quality attributes here, my three critical process parameters, my factors here, and I can explore the model as usual. But now I want to try to figure out a design space for my manufacturing process. Now I can easily do that by going to the production profiler, little red triangle menu, and several down. I see there's a new option for Design Space Profiler, and if I select that right below the Prediction Profiler, the Design Space Profiler will appear. As I noted, if I hadn't already had spec limits attached to my responses, it would prompt me for that. But now I can see that it's brought them in from my column properties. You can see right down here. It's also brought in an error standard deviation. These values are coming from the root mean squared error of my Least Squares models. Y ou can see here, RMSE is here, is the same value for in-vitro release as the error standard deviation here. Of course, you can change these, you can even delete them. But we highly recommend that you have some error for your predictions since your predictive models are not perfect, not without error. Okay. The first thing you might notice about this profiler is that it looks a little different in that each factor cell has two curves instead of the usual one curve. That's because we're trying to find factor limits. W e're trying to find an interval, we're trying to find the operating region, the design space where we're optimizing our operating region. The blue curve— we have a legend to help us— this represents the in-spec portion as the lower limit changes, and the red curve represents the in-spec portion as the upper limit changes. You can see how if I were to change the upper limit of emulsifier, it would increase my in-spec portion. That would be a good thing. That's how that works. Also the in-spec portion, you don't see the value over here on the left like you usually do, but it's right over here to the right of the cells. It's initially, 79.21% of my points are in-spec and that's in-spec for all of the responses to all of the CQAs. If you want to see the individual in-spec portions, you can find them down here next to the specific response. Also, you can notice this volume portion is telling me that I am currently using all of my simulated data and that's because the factor limits are set at their full range initially. To be able to change the factor limits or try to change the operating region, you can either move the markers as usual or you can enter different factor limit values here in this table or right here below the cells or you can use these buttons . I really like these buttons. If I click on Move Inward, it's going to find the biggest increase in in-spec portion. It's going to find the move that gives me the biggest increase. It's going to find the steepest upward path . Move Outward would do the opposite. It would find the steepest path downward. If I click Move Inward, notice that my emulsifier lower limit has increased from 700 to 705, and my in-spec portion has increased to 81.95. If I click it again, now my lecithin lower limit has increased from 30 to 31, and my in-spec portion has gone up to 84.5. I can keep doing this. But before I keep doing this until I find the desired in-spec portion that I like— and I'm happy with the factor limits, I think it's a reasonable operating region— there are several options in the Design Space Profiler menu that I like to look at. The first one is make and connect to random table. W hat this does is it creates a new random table of uniformly distributed points. You always want to add random noise. It's going to use the same random errors we used before. I'm going to click Okay. Now, I get this table of 10,000 new random points, and they are color- coded. The ones that are marked as green are in- spec, the ones that are red are out of spec, and the ones that are selected are within my current factor limits, my current operating region. It's useful to look at the table, but I really like to look at these graphs that are produced by some of these saved scripts. If I run Scatterplot Matrix Y, it will give me a response view of all my data . The shaded region that's green here is the spec limits. T hen I also like to look at the Scatterplot Matrix X, which gives me the factor space view. It's nice if I can look at them both at the same time. While I'm altering my factor limits, if I click on Move Inward again, you can see how the points change . I find it even more useful. You also see how the factor space changes. I find it even more useful to hide all the points that are not in my current operating region, then I don't even have to look at them. Now, as I keep clicking on Move Inward, you can see how that operating region is shrinking. If you only want to be concerned with the out- of- spec points, you can click on Y Out of Spec, and that will only show the out -of- spec points that are occurring. Notice that my in-spec portion, as I keep moving my factor limits in, is increasing . I'm going to keep going until I either hit 100% or my operating region looks like something I can't that isn't feasible, that I just won't be able to attain. I'm going to keep clicking Move Inward. Things still look good. Move Inward, just going to keep clicking it. Okay, I hit 100, and I still think that these factor limits represent an operating region that I think I should be able to attain. To be able to look at that further, I can send the midpoints of these factor limits to the original profilers, see what that looks like. I think that looks pretty good. I can also send the limits to the simulator in the Prediction Profiler, and I can decide to use different distributions. I actually think that my critical process parameters follow normal distributions. I'm going to select this Normal with Limits at 3 Sigma . It turns on the simulator, and it sets my distributions to normal, and it figures out the mean and standard deviations for these limits with Sigma, 3 Sigma. Of course, you can change all these values as you think seems fit for your own situation, for your own manufacturing process. You can change the distribution, you can change the mean cedar deviations. I'm just going to leave it, and I'm going to see what simulating, what the normal distributions looks like. It looks really good. You can see my defect rate. When I keep hitting Simulate, it's often 0. I also like to simulate to the table to be able to just get a view of what my capability analysis would look like just as a sanity check. I f you come down here, you can simulate the table, and it's going to use these normal distributions for the critical process parameters. It's going to use the same errors for your predictions as we used before. I'm going to click Make Table, and when I do that, it automatically creates some scripts. One of them is distribution. If I run that, I can very easily look at my capability reports because I saved my spec limits as column properties. I see that the capability looks, at least for the simulated data, it looks really quite good. So I'm pretty happy with this, even though this is just on the simulated data. Of course, I need to check the real data, but I'm really happy with what I'm seeing so far. I think I'm going to use these limits as my design space. Now, just to note before I go further, I have a good situation here, but let's say that you didn't have a good situation where your in-spec portion wasn't where you wanted it to be, and you really can't adjust your factor limits anymore. You could do what- if scenarios by changing your spec limits or your errors if you think that is something that could reasonably happen. But I have a good situation, and I'm happy. I am going to use this option Save X Spec Limits, and that's going to save these factor limits back to my original data table, to my critical process parameters, so I can save these factor limits. When I do that, when I go back to my original table, you can see that those factor limit settings have been saved as Spec Limits to my critical process parameters. I find it really helpful to be able to look at this design space in terms of the contour responses and the acceptable region. I've already saved my predictions as formulas and I have a script saved to run the Contour Profiler. I'm going to run that . This is going to give me my contour responses for all combinations of my factors, my critical process parameters. I don't know if you can see the faint rectangles, but that is my design space as defined by those factor limits that got saved as spec limits on my critical process parameters. The shaded colored areas, these are my spec limit response contours. You can see that my design space is nicely within an acceptable region for all these contours . It's even further in. It's not touching them. That's because we added that error end for our predictions. I'm really happy with this. Okay, let's get back to PowerPoint. I just want to give you a few takeaways about the Design Space Profiler before we wrap up. First of all, that in-spec portion that we saw in the Design Space Profiler shouldn't be taken as a probability statement unless you believe that your factors, your critical process parameter factors, actually follow a uniform distribution because that's what was used to distribute them. Also, the Design Space Profiler is not meant for models that have a large number of factors or very small factor ranges because of the simulated approach that it takes. It's also recommended, as I've mentioned several times, to always add random error to your responses because your prediction models are not without error. And finally, I just wanted to make a statement that even though this was motivated by pharmaceutical industry, it really is applicable much further than that. In any case where you want to find an optimal operating region and you want to maintain flexibility and quality, then this can be helpful. There were many things about the Design Space Profiler I didn't have time to show. I really hope that you will check it out. Any questions?
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Easy DOE: Easy Enough for a 7-Year-old? (2023-EU-PO-1231)
Saturday, March 4, 2023
JMP 17 introduces the Easy DOE platform, providing both flexible and guided modes to users, aiding their design choices. In addition, Easy DOE allows for the DOE workflow from design through data collection and modeling. This presentation offers a preview of the new Easy DOE platform, including insights from a 7-year-old using the new platform on a DOE problem of her choosing. Hello . I'm Ryan Lekivetz , Manager of the DOE and Reliability team at JMP . And I'm all Rory Lekivetz. We're here today to talk to you about Easy DOE . The question is it easy enough for a seven- year- old? N ow that you're eight years older you're a lot wiser to answer that question. For those of you who don't know about Easy DOE, so it's a new platform and JMP 17 . Now , the idea with Easy DOE is it's going to be a new file type that encompasses the design through the analysis of a designed experiment . No more do you need to worry about splitting up , going from the DOE platform to a data table and then running the analysis separately . Now , the idea with Easy DOE is that we're trying to aid novice users through that entire workflow . There's going to be a guided mode where we've tried to add hints and useful defaults to guide those users while at the same time having a flexible mode for those who are more comfortable with Easy DOE . Now before we started doing this idea with Easy DOE and running our experiment, I did talk to Rory about the daily workflow . I f you open up the DOE documentation , we outline this idea of a Easy DOE workflow which goes through the described phase , which is where we identify the goal and the responses and the different factors, specify where we're looking at our model . We create the design , collect the data , fit a model to that data , and then use that model to predict . Right now , if you think about the way traditionally we've done this in JMP, at that design phase is where we create the data table . Using that data table the experimental go and collect the data and then perform the remaining steps ending it. N ow what you'll see in Easy DOE , there's the tabbed interface where each tab represents one of these steps in the DOE workflow . Now what was the experiment that we did ? Paper airplanes . Rory had found a website that talked about different ways to create paper airplanes . You want to tell them what was the response ? What were you trying to measure ? We were trying to measure the distance which was inches . What factors did you end up deciding that we could change ? For factors we decided on war plane type, paper type, flying force and paperclip . Yeah . Now you to tell them about some of these different tabs . Okay , so let's start . What was the define tab ? The define tab was where you got to choose your factors and your responses . That's right . I should mention here as well that when we were using Easy DOE , I left Lory in control of the entire platform . She launched it . She was the one entering everything and clicking between tabs and all of that . I think after the define tab , we moved to the next. What was that next tab? Model. F or the model tab , you had to choose w hich one of these four was the best for your experiment. Now I'll say too, on this one this is where we had to talk a little bit more about what these different model types mean . Of course , for a seven- year- old and even an eight- year- old , now that idea of understanding interactions can be a difficult thing . Now , the main effect versus the interaction . One of the nice things was the website that we had found about creating paper airplanes . It talked about how some of the different types of paper airplanes do better when you throw it hard versus light . It already had discussed that idea of interactions, so that's why ultimately, I helped her decide on picking that two- factor interaction model with the main effects . Once we had that model, then what happened? Then was the design . The design shows you what you're going to be making . Since we were doing paper airplanes and we entered the factors for tight paper throwing for some paper clip , then it sounds like different types , different papers, different throwing forces like route and paper clip or no paper clip. Yes , I think we made the 16 different paper airplanes and so each one was a different one . I think we put a number on it . Is that right ? We label it with a number one . Yeah . Then what happens after we have that design, what do we do with that ? Then we do good data entry . With data entry is where you enter in how many inches you want. Yeah . I think we went outside and we took those paper airplanes and we flew them anyway and then just measured that . Yeah , that's right . W hat happened after we had that data entry ? Then we go to be analyzed . Analyze is where you figure out which ones were the best. Yeah , which ones really were impacting that distance flown . Now , I should mention here , so this is a novel thing in Easy DOE . The confidence intervals for each of our different effects are clickable . All right, I had actually thought that this was going to be a really difficult time to talk about the analyzed . But as soon as we got there, so it starts with all of the terms in the model and very quickly figured out that you could click on them . She looked at the ones that were close to zero and just removed them . On the top was actually her model . She actually picked a much simpler model than even what the best model . You'll notice there is a best model one . But one could argue that I actually might even prefer her model to the one that was picked by the best model . But again , still a very nice way to play around with your model and see what happens if term enter o r are removed just by clicking on those confidence intervals . T hen after we moved to the analysis , what was that tab they had? The predict tab was where you could see which types or which t hings were the best . The best look like it would probably be a dirt metal construction and differently for us since it was like, the hard in blue light was like… Did it matter or not really? Not really . It would be like you could do hard or light in your paper . I should mention here, so it was interesting to see she hadn't really seen the prediction profiler so much before . I mean , wasn't familiar with it . S he did have to be told to click in there to see what happens . But even for a seven- year- old , it's interesting to see once they have that sense that they can click within that prediction profiler , she was really able to get the hang of it . J ust some final thoughts from Easy DOE . I asked Rory a few questions ahead of time . What would you like to tell people about Easy DOE ? It was really fun . Yeah . If you were to do this experiment again , would you change or what would you change ? The factors , maybe different days for the weather . You think like it might be windy on some days and not on others . F or my own perspective , you know , so she was actually able to complete this with minimal help from me . I mean , she was in control the entire time of the Easy DOE platform . A lot of these different choices she was making on her own . Even when it came to the factors that she picked . As well she actually did help us find some usability issues . There were pieces like in the design tab that I think we improve throughout because of users trying this out not just for her , but as well as other users that we had in the DOE program . The model she definitely needed help with , but the analysis was easier than expected . Just some references , acknowledgments . Really , I just want to thank all the members of JMP that helped in the development of Easy DOE . There's a huge list that you'll actually see . We have a Discovery America presentation there as well , where we talk about this in a little bit more detail . Again , all the feedback from external and internal users that have seen this before the release of 17 and since it's been released . Thank you for your time and joining us today . We hope you'll join us during Discovery where we can discuss this poster . Not sure yet if you'll be able to join us , but I definitely will be and hopefully you as well . Thank you . Thank you .
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JMP Into Retirement Using the Reliability Platform in JMP® (2023-EU-30MP-1276)
Saturday, March 4, 2023
The powerful Reliability Platform in JMP® is often overlooked and underutilized. This talk demonstrates some of the basics of the Reliability Platform in JMP by answering the seemingly simple question, "How long will my retirement endure?" Why guess at this extremely important figure when planning for retirement? Use JMP to explore this question! This was accomplished using Reliability and Survival Fit Life by X and historical data from retirees. Uncertainties of this prediction were also quantified. The optimum retirement age was addressed, considering that retiring earlier draws less income for a longer period. In addition, JMP was used to visualize the employer's retirement tool to optimize the most financially desirable retirement age and explore the most enjoyable chapter of life — retirement! Hello , my name is Don Lifke , and I'm with Sandia National Labs , I'll be presenting on Reliability Platform in JMP . The example I'll be using has to do with jumping into retirement . Sit back and enjoy . And let's see where this leads us . A little bit about Sandia. We are , federally funded research government owned, contractor operated facility . I 've been at Sandia since about 2005 , roughly. I'll tell you a little bit about what we do at Sandia. We work primarily on nuclear deterrence . Six major programs that we're working on right now, I won't go into the details of these , but all of these programs that you see on my screen are programs that I worked on and have actually applied JMP to all of these as well . The reason I'm presenting data on retirement is because a lot of the stuff that I do is just too sensitive and I can't present it in this particular environment . So, we're going to use some fun data that everybody can relate to . Naturally , this is a recording , so you hold your questions to the end . But this is the same presentation I'll be giving live at the conference . L et's talk about retirement . Why is it something that is so certain but has so much uncertainty in it that makes it a little bit hard to plan ? I'm not sure I would want to be without that uncertainty . I prefer a little uncertainty in that . It does make planning for retirement a little bit difficult . But I guess on the bright side , if I were a cat , it would be even more complicated. By the way , that the GATO here is a repairable system , and that's a whole new topic in general . We're just going to be doing systems that are not repairable , like cats . What we can do is we can use the Reliability and Survival tool in JMP. Some of the screenshots that you see maybe are from an older version of JMP. This was created pre-COVID . Now that we are on newer versions of JMP there might be some slight differences in what you see and what I've read from the screenshots , but should be fairly similar anyway . But we're going to be using the Reliability and Survival and the Life Distribution and Fit Life by X in JMP to do some analysis of some data. I'm not sure about where data is coming from . At Sandia , we have a Lab News that comes out, every couple of weeks and in that Lab News they like to post retiree deaths of the people that we worked with for a long time, just to let us know that they've moved on and gone to a big R&D facility in the sky . So, I thought I could use that data to help me plan for my retirement . I grabbed some of that data and I took data from four different periods , of Lab News archives . I pulled some data from 2001 , 2007 , 2013 and 2018 . The number of data points that I grabbed from each of those is here . We'll look at some of these data and see how things are changing through time as well . But anyway , the number of data points is pretty significant . This little clip , this little picture on the right is a little trivia there . If you look real closely at the violinist on the left , some of you might recognize him or let's leave that up to you to figure out . Maybe I'll sing it . It's actually my partner , Claire . She's a pharmacist . But also a beautiful opera singer . T he nice thing about using the retiree death data for me is that that population is a better representation of my lifestyle . They tend to take less risks , lead little more conservative lifestyle and a similar income and education level , of course , where the best and the brightest are left at the probably at the bottom of that list , but at least I'm in that group . It's an honor to be working with the folks at Sandia . Another nice thing about using the retiree death data is, it only includes those who actually made it to retirement . I don't really care about the data for not making it to retirement . And I apologize to my kids . I'm in Albuquerque , New Mexico right now . For those of you who love Breaking Bad and know that Breaking Bad was primarily filmed here in Albuquerque , and so I'm a big fan of the show , so I've got to put a little bit of fun stuff in the presentation . That's why I threw a little bit of Albuquerque reference in there for those of you who are Breaking Bad fans as well . And some Better Call Saul stuff to . Let's go right into JMP and start doing some of the analysis . I'm going to bailout of that and I'm going to open up my data file . Let me see some screens around here . It'll take a second to load on your screen . You should be seeing my data file . What you see are the columns of age in the Lab News . That's the newspaper that I got the data from. The bi- weekly date and I broke that down to year . And these other columns I'll talk about in a little bit . Right now , let's just look at the age and the year . Let's just look at the distribution of the data and see what it says . If we analyze the distribution of age , I'm just going to analyze distribution of age in general . If we look at that , this is what the distribution looks like . You can see a sort of a skewed left distribution and we can really see a little more detail on this if we go into some of the display options in JMP. This data actually best fits Weibull distribution , a two parameter Weibull distribution , which I'll show on some of the future screens . But what we want to do is take this data and use the Life Distribution to look at it . I'm going to drag over my PowerPoint here and show you that I set these distributions in the interest of time rather than just actually doing it . L ooking at the distributions of the four years , the four categories of years , which are , 2001 , 2007 and 2013 , 2018 , all of these fit a Weibull distribution fairly well . I wanted to check my assumption when I start analyzing the data in Life Distribution . Then I fit them to a Weibull distribution . Let me open up ... Let me give you a little bit of background on the Weibull distribution , that will help you understand some of the data that I'm going to show . This is actually a Weibull distribution... Weibull distributions generated using JMP's formulas , actually . I've taken distributions with different health plans and different pay DOEs just to show you what the alpha betas do with the Weibull distribution . Let me run my script on this and show you what these look like . Basically, I'm just doing a Fit Life by X in JMP , and I save the script to the data table . This is primarily just to show you what happens with Weibull distributions when you turn on a local data filter here and show you what the parameters do with different alphas and different betas . If you have Weibull distributions where... I'm going to choose the three that have a beta of one. The beta Weibull distribution, essentially determines this is the spread of the distribution you think of in terms of standard deviation . When you plot the data on a Weibull curve , you will see that they all are parallel lines and they're basically just scooting across the X axis here . The beta is the slope of this line , which is the spread of the data . If I look at three curves that have the same alpha but different betas , you'll notice that they're all centered at about the same point . What the beta is doing is changing the spread . Alpha is changing the location of this. They all across this point , which is actually at 0.632 . I'll talk about that here in a little bit . But though the characteristic lifetime is the alpha , and that's the point where your line crosses 63.2% , which comes out to be one minus one over 80 if you want to get into the proof of it . The beta is the spread of the data, the alpha is the location of the data. Scale and location, it's sometimes what they call it . Just a little background on that . I'll swing this PowerPoint back over here and show you and summarize slide what I mean . These plots have the same alpha . They're all centered at the same place . The three that have the smaller rectangle around them are the same beta . So, they have the same spread , but they're located differently because they have different alphas . Just a little brief tutorial on what the beta distribution parameters do to the curve . Let's just look at all the data on a close up. Example , probable curve data , and let's just look at the age parameters . If we analyze the Reliability , Survival and just look at the Life distribution of age ... What we see is linear scales on the X and Y , but we can actually determine which distribution system fits here . And what we find , of course , is that the Weibull fits best . This is what the Weibull distribution looks like, on all of that data crammed together . Using the distribution profiler , I can actually manually scoot this over and say, I want to be 90% sure I don't run out of money . I better plan on a retirement age of roughly 92 or so . About 92 to be 90% sure I don't run out of money every time . That was really the focus of this day in trying to help me plan my retirement age . You know , they always tell you how long you want to plan your retirement for . Of course , we don't know . Usually we just take a wild guess , but this is a little bit of data driven decision making going on here . What if we look at these through the four different years , we can use the same platform , the Reliability Survival or Fit Life by X. We can look at the age versus the actual year here . Let's see how the years look differently . Let me turn on the density curves here so you can see where those fall . I want this to be a Weibull distribution . We can also turn on quantile lines to see how things change through time . This is the 10, 15 , 98% quantile line . It looks like maybe maybe from 2001 to 2007 things have change a lot , but about 2013 , maybe the people are living a little bit longer based on the 2013 and 2018 data . We go down here and look at the details of this Fit Life by X . You can see the plots all separately here . These little profilers are fun to mess with here . You can see the age , the probability of failure . Looks like I've got the long run and the probability of failure is... I'll just put a 90 here . But we can see that it looks like we are getting a little bit healthier because this Probability is going down through time , at least for 90 years old . There's a lot of stuff in here that you can tinker around with . I don't have the time to show you all of that , but what I want to get down to here is ... This location scale . This little test here is telling me , are my locations different , assuming we don't have the same data , the same failure mechanism , which in theory we do , right ? We all have the same failure , but the physics of the human like failures should be constant . And so our data shouldn't change much . But assuming we have the saving betas and the alphas changing , in other words , is the location of these changing ? And the data says , yeah , that we're rejecting the null hypothesis that the data are not scooting across this X axis through the years . There's a change . Now looking at the location and scale , it looks like that's marginal . I'm going to talk more , when we get down to the Weibull here, the actual Weibull data . The Weibull looking at is there a difference in the beta , in the slopes or in the spread of the data year to year. It's right on the edge of being rejecting the null hypothesis that they're located , that they're actually the same on the slopes . If you want to look at different distributions through the years , you can actually do a statistical test to see if things are changing through time , the distributions of your reliable data . Right . So, what am I forgetting ? Let me slide back over here to the PowerPoint . What I'm not doing , I'm not considering censored data . I don't have the data for everybody who's still alive . The retirees are still alive , but I just don't have access to it . So, I thought , well , what's that going to do to my analysis ? Well , I can play around with the data I have and go back in time . For example , I can go back to 2007 and I can treat the 2013 and 2018 data as censored data because I know that those retirees are still alive . Just to see how it affects my analysis , the fact that I'm not including censored data . I did do that . I went back and messed around with that . I'll show you a little bit of it , but I didn't really find out much . As our great friend George Box that models around the practical questions , how wrong do they have to be to not be useful ? And I really didn't find anything useful in doing that . But I will show you at least what I did . To convince myself that not having that the data for the retiree still alive, is not a problem . If we look at the ... Let me tabulate my data real quick . If I look at the age , the year of the data versus whether or not I suspended the data , what I did is I took the 2018 data and suspended it and worked my way back to 2013 . We did an analysis on 2013 using the three years of data and then using the 2018 data as suspended data . Of course , I had to take the data from 2018 and I had to subtract five from it because that would be the age in 2013 . I n order to include that suspended data , I had to adjust their age accordingly as well . But I also did that for 2007 . Let me show you in tabular format , I created a column called Suspend 2007 where I took the data from 2018 and 2013 and also suspended it . I took out my data of known deaths and half my data as suspended data , people who are still alive . You can see that ... Let me show you what that did to the data . For the age on 2013 data still alive , you can see that it's basically the 2018 data minus the five years . If I look at the actual age here . If we look at the mean age , basically this is the mean age for the 2018 data . By suspending , I basically just subtracted off the five years here . And treated out the suspended data . And the same goes for the 2007 data . T here instead , I believe , I simply just subtracted off nine years, and five years for the 18 to 2013 data . When I wrote the analysis of those , I'm just going to show you on the PowerPoint slide what it looks like because it works out convenient for me to jump back and forth between these two to show you the difference . What I found was that this was my original data and then this is the data treating 2018 data suspended and doing the analysis of 2013 data . You can see that the area of interest where I'm really concerned about is where I'm crossing that maybe 90% probability . It didn't change much at all . And then this is throwing the 2018 and 2013 data assets during the analysis based on 2007 data and the 2001 data. You can see that it really doesn't change the curve much in the area that I care about . That gave me the comfort that I really am not missing much by not censoring out the data . I mean by not having this censored data . How did I calculate my 90% confidence intervals ? I'm going to just show you in PowerPoint how I did this just in the interest of time . I can use the quantile profiler and look at the 5% 95% probability and calculate my 90% confidence intervals . Typically , what you'll see for data and in this case , it's 66 years old, 96 years old . But really what I care about is the upper limit . I only care about my 90% probability . And in this case it was age 94 . Based on this analysis , I now know that I want to be 90% sure I don't run out of money . I should plan to live to be 94 . This is based on my historical data . Quantifying that uncertainty , this is basically ... A little bit humour tone in . The next phase in this was to try and figure out ... I'm going to throw in a little bit of bonus material here beyond the Life Fit situation . Here we have this tool called the Pension Tool. We could actually put in the year we're going to retire, and we could also assume a salary increase and a non base , which is, essentially, what you would call a bonus in private industry . We could put those into this tool and it would spit out our pension, our estimated monthly pension . I thought , well , that doesn't help me much. What I want to do is, I want to reverse- engineer that tool so that I have the profiler to tinker with . I went into the JMP and I created a Response Surface model , and I basically input three different ages at retirement , 62 , 65 and 68 . I did three different salary increases and I tried three different based awards as well . Let me pull data over here . This is what the data looked like when I ran the experiment . I'm just going to show you the the screenshots from JMP rather than do an actual JMP analysis because I'm like 20 minutes already and I want to make sure I have enough time to cover all of this . When I created this experiment , you'll notice that the runs are sorted . That's okay because I'm doing a computer simulation so, I don't have to worry about lurking variables like the temperature in the room , the humidity and the operators . I'm going to get the same answer regardless of the order that I ran this . So, it's easier for me to do the tool to run them in order . But when you do that design of experiments , you want to randomize your run . Do not run them in order out of convenience . If you do, you feel, okay, your set of strains will tell you that you can use the how to set easy, how to changes with the change, feature in JMP to make sure you do that properly by using some blocking . My inputs were all orthogonal . I covered all combinations of the... My age at retirement , the three different ages and the three different month based awards and the three different salary increase percentages . This is the look at the analysis you can do when you're setting up your experiment in JMP. You can make sure that your main effects are only correlated to themselves . And that there's minimal correlation to other variables , in two factor interaction is also a squared term since I did a Response Surface model. But the net result was a nice prediction profile and I'm going to go ahead and just run this and show you . Prediction profiler . When I set up the experiment , I tell JMP. When you set up an experiment in JMP , it's nice because it gives you these scripts already in your data table . I'm just running the model data table . And JMP says , Oh , I know what you want to model , because I was here when you set up the experiment. And it gives me my Y and it gives me my Xs . It runs my a nalysis for me . I can now use the profiler JMP, rather than having to put in an age, salary increase, and a non base award into that tool and getting one number out . Now I have the profiler I reverse engineered their tool essentially. I can see what my monthly benefit is going to be versus a salary increase , non base, where you see that non base award doesn't really have much effect. Salary increase , not much effect . And of course , age as we would expect, have the most effect . What do I do with this information ? Well , what I really care about is my lifetime benefit . And I'm also I'm concerned about inflation . So, I added some formulas to this . I added a lifetime benefit , which is just my monthly benefit times how long I'm going to live . If you want to look at the formula , you can see it's basically my monthly benefit times how long I'm going to live . And that's my lifetime benefit . That's what I really care about , is how much money am I going to get in my lifetime . I know if I retire earlier , I'm going to get less money . That's a no brainer . But I want to know , is there a point of diminishing returns. W hen I looked at the age at retirement , my lifetime benefit for 80 , 84 and 98 created three separate columns for those data will be the column. All these data wil l be provided to you as well , if you want to tinker with it . But what you can see is a lifetime benefit for aging . It is actually a point of diminishing returns. Where I might as well just retire at 65 because I'm going to get less money , but I'm going to get it for a longer period of time . Now , as that starts to increase, if my lifetime at age 84 , turns out my lifetime benefit would have been a little bit better hanging on there . And as I get older , of course , I'm better off waiting as long as I can to retire . But I really don't know . I don't know what that number is . My best guess is 84 . That was the fiftieth percentile on my fit . If I want to be really conservative , I'm looking at 90 . But I also wanted to look at how inflation matters . I looked at the present value, I put a formula in JMP . There's actually a formula for present value . Let me explain [inaudible 00:26:05] I'm going to show it to you real quick . In JMP you can actually use a formula similar to what Excel has and calculate this present value. And that adjusts for inflation . This is what your money's worth now, relative to what you're predicting , the future inflation rate is going to be annual inflation rate . At 0% this present value is just your lifetime . The number of payments you're going to have through your lifetime times the payment amount essentially , but you get penalized as inflation goes up , it becomes less and less . If I look just at the inflation present value data ... If I just model those , what I noticed was ... I'll make these little bigger so you can see them . I noticed that when my salary increase is smaller, lower ... This is a two factor interaction . You can see the slopes changing . That was some small salary increases , which are probably not unrealistic in the coming future . There is a point of diminishing return when we penalize for inflation . At lower inflation , there is not that big of a benefit to wait to retire . Once I hit about 65 or so , it's starting to flatten out . Really the difference isn't that big . If we look at the predicted value , these are not my actual numbers . These were based on censoring the data using a pitch factor of $4,000 a month , which is the typical retiree income . I noticed that as I weigh , it flattens out so it doesn't really pay to wait to retire . I'm using this information to help me decide when I really want to retire . Right now , I'll be 62 in May , so I'm starting to approach the ability to do this . Probably going to wait till 64 , 65- ish to where the difference in my present value is not that big . That's a little quick bonus on how I use the designer experiments feature in JMP to reverse- engineer this . In summary , I looked at the retiree death data and I set the Life Distribution and Fit Life by X, I looked at four different time periods and noticed maybe we did get a little bit healthier and healthier in 2013 , but by 2018 that have flattened out . I do have some 2021 data that I don't have in this presentation, but next year at the conference in Spain in March , that it's actually pretty flat from 2013 to 20 18 to 2021 . We're really not getting healthier , living longer . That's going to help me with my decision . I took a custom designed experiment as bonus material here , and I reverse- engineered this web based applet that we have and used the profiler to replace that one data point at a time . A really cool thing , you can do in JMP . I would take questions here if were alive . And with that , I want to say one last thing . I really want to dedicate this presentation to my brother , who I lost a couple of years ago to brain cancer , who was only 15 months younger than me . He was a fellow Sandi and also a fellow JMP user . Some of you may have met him at the JMP conference and so I'm dedicating this to him . Thank you very much .
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Don’t Lose Time Anymore: Automated Access, Visualization and Evaluation of Production Environments (2023-EU-30MP-1347)
Saturday, March 4, 2023
This presentation demonstrates how to access different production plant data to look into all corners of a production plant for quality assurance. JMP was used to access production, process, and quality data with one click. The presentation shows how data accessed via REST-API from an existing application was optimized by data import, data cleanup of input tables using JSL, and visualization of automated dashboards. Specification and control limit management for automated reporting was critical. Also, where data import possibilities were limited, a combination of Python and JMP was used to import process factors and responses for each production step of interest in mass production plants. A full range of quality assurance tools will be demonstrated, which could be used to begin helpful discussions with production teams for continuous improvement plans and PDCA Cycles. Hello, everybody. My name is Ole Lühn and I am member of Global Quality Assurance at BASF. Today at the JMP Discovery Summit in Europe 2023 in Sitges in Spain, I want to talk about my topic, which is called Don't Lose Your Time Anymore, Automatic Assess, Visualisation and Evaluation of Product Environmental. So how to get fast access to your production sites. And here in the background, we see the site I'm working at, which is BASF Schwarz heide in Germany. On this picture and on this slide, you see all the sites of BASF worldwide. So there are Verbund sites, which is a combination of different production environments. We have R&D centers, production sites, and regional centers. And for Europe, I am located in Schwartzheide. And on the next slide, I will show you a little bit more detail where Schwartzheide is located. I am working, in fact, with different sites closely together, which are Ludwig shafen, which is the main part or the main site of our company with approximately 39,000 employees. And Schwartzheide is located roughly 140 kilometre south of Berlin, and we have about 2,000 employees. Today at the JMP Discovery Summit in Sitges, which is located a little bit north of Tarragona and south of Barcelona, we are having the Discovery Summit. I explicitly showed Tarragona here on the slide because I have also worked with them to do where we formulate our end -use product, which we sell to the market. And on the upper right corner, you see a glance of Europe side from BASF, and for the color code, please see the previous slide. I am working in Schwartzheide, and here you can see the picture of the site as of today. So the plant where I am working at and where I am working for is in the orange rectangular shown here on this picture, and on the lower left, this picture shows the view from my former office. I had a look out of the window and could see the plant where I was working for. So at that time, I was still located in a different office. Of course, I want to know more about what is happening in the plant, and that is what my talk is about, to have access to data in the plant when you're not currently working there. So the introduction of BASF and me, I am a member in the business unit, Agricultural Solutions, in the team of the Global Quality Assurance. And I am a JMP user since roughly JMP 9, so it must be something 2010. My task is to assure production processes and times of quality, so quality assurance. Also, I am working in quality management and I am a ud itor for different ISO norms, and I am involved in nonconformance management and deviation management. For the final product, I'm responsible for the release, and I also have to sign each COA that we pack and that we pack to the product shippings around the world, and on each COA, I have to sign. So I want to know what is happening in the production. So from a statistical point of view, I want to know if there are differences in the production or in the production process and the production environment. From a practical point of view, I need to evaluate if these differences do really matter. I want to know the details of the production as fast as possible and as easily available as possible, and that's why I started this topic a while ago. My goal is to have a proper root cause analysis, preventive and corrective actions in nonconformance management. The work started roughly 2020 when we all realized that the corona pandemic was developing. And like in many companies, there was a restrictive access to the plant. So while I was sitting at home, I was asking myself how to get access to the data from the plant without being on site. So how can I see what happens in the plant when I'm not really sitting in the plant? And how can I be part of the production and the production teams when I'm not available around? So the idea is that I go digital to the plant and realize what happens. Without the data access control V and control V, which takes everybody notices in our community days, maybe, or hours to get a real evaluation done. So my idea came up while I was participating on a seminar on the tablet production process in the JMP lecture courses, and my goal was to have the highest degree of automation in my data access in seconds. Here is what it is all about. So here on this picture, you see a general overview of our plants. In fact, we have three plants which are producing parallel our product. And the very general value stream is shown on the top of this graph. We start with raw materials, we have intermediates in our process, and we go to the final product. So this data of the last three processes is available via a REST-API interface where I can get the data from. All the data is prepared in one table. I have the time, I have my final lot, I have the QC data of my final product, and for my intermediates, I have different factors and different responses available. What I mean with factors and responses is shown on the upper right picture, which I took from the JMPs course. This work was started with a colleague from me, Bernd Heinen, who helped me out in writing the scripts and preparing my data access. This is what I want to show you now in JMP. We prepared our scripts in a very easy way. We put the REST-API address here in our scripts. We do some scripting instructions. I had a problem with the data time format when this table was imported into JMP. I was asking in the community and someone gave me the solution for my time formatting problem. I add some specs to my columns and I save it on a drive. I always save it as an Excel file as well. The data which is shown here is the data for one of my plants. I opened this table now. It's table number two. Here I have my time of different processes in the plant. I have the lot of my final product. I have the response, the QC data of my final product, responses and factors of previous processes. Firstly, I'm interested in the final product. I started to plot the data in the control chart of one of my responses for my final product. However, it is an X -bar and R -chart. The reason for this is that from my last intermediate, five lots are put together in the final reactor, and therefore, the X -bar and R -chart is created, and not the individual and moving range chart, which I am interested in. So I need to do a little bit of data preparation work. The outcome of the data preparation work is shown on the lower part of the slide where I have the access of all three plants parallel, I can do my data preparation. I use the function in JMP to concatenate these data tables and I can plot easily an overview of my final QC data in this video. I use the Fit Y -by -X platform for the three plans, use the column switcher and recorded a video. Everything is possible within seconds. The idea of my work started when I had a look at the tablet production processes or the tablet production process from the JMP lecture. Here we have this red triangle which shows us suspicious values in a production trend. My idea was to also create in my data table a column where I add suspicious data or suspicious results and color it in a binary format. I changed my continuous variable into a binary one, like good or bad. For me, it is more easy to discuss this with the team from statistical point of view and everybody in our community knows this as well. There are more powerful ways in order to get this data analyzed for regression, for example. However, I choose this way. I will show you what I mean. I prepared this in my slides. I wanted to have these indicators, good or bad, or suspicious or not suspicious in my data table, so this data table is taken from the JMP library, the tablet production process. And in the end, I had my data prepared like this. I added a column to my data table. Here, because I had five lots of my intermediates, I needed to split my data first and to evaluate only the final product. I added a column where I evaluated on my individual and moving range charts how is the data distributed? And here we can see that my internal specification is far above my data. However, there are suspicious events in the production process. And this data I can also plot in a dashboard here where I cut off above the upper control limit my data. With this data prepared, I can have easy discussions with my team and tell them, "Look, here, what happened here? I just added time and say roughly around this time, we had a suspicious event here in our factory. What happened?" Let's go back to the JMP table or to the presentation. I created this column, good or bad. And from a statistical point of view, we know that regression is better. The script in order to evaluate all this data at once within 20 seconds of time is containing actions like to split the data, make a summary, analyze the control limit, summarize my control limit in an extra table, introduce this column. First I did it manually, but I now had a nice script to do it automatically. I merged my data, my summary back into my first data table, which I extracted from my REST-API interface here, and it's done. Everything was in seconds, and from the JMP team, Florian helped me to do this here. Here is just the proposal for me and for people who work in the quality department, how to discuss these graphs with the team. Of course, there are different ways how to analyze data in JMP. I like most the Fit Y -by -X platform, and I also like the hypothesis testing ways which are shown on the video here on slide seven, where I plot the three plants parallel to each other and go through all my QC data of interest and compare it. Furthermore, I also like the process performance graph which is available in JMP. But one way of analyzing my data is also that I can easily plot Pareto plots of my out -of -control events. I have the data prepared. We go to the table. We have only the values which are not okay or above the control limit selected. I click on Pareto Plots and I can discuss this easily with the team and tell them these two variables are most of the time out-of-control. What do we do? I can also compare three plants at once immediately. It's more or less the same, but I can have a look here and create meetings and discuss about suspiciencies between the three factories. So obviously the factory which is colored red, there the less out-of-control events are created. This is part of my story to have fast data available here to to be discussed in the teams. Of course, I know that there are different options in JMP, so we can do regression, we can do predictor screenings, and the data table for the predictor screening is also shown here. In fact, I have my final data table which I extracted from the REST -API interface, and I have this out-of-control column added to my previous data, and I can do a predictor screening and I can have a look. Where are the factors in the plant which influence the outcome in my final product first? Of course. Okay, he's doing the calculation at the moment. Of course, these kind of things are difficult to discuss, and it's also not part of my job to discuss here about process suspiciencies. My topic is quality and I want to reduce the number of off -spec events in the plant. I'm talking about off -spec and not out-of-control anymore. We had a time... I go to the right part of the slide. We had a time in the plant where we created off-spec events. I did a partition with the data and the data told me, "Look here, this is suspicious, below 12,000 or 13,000. Below this value, it is most probably that we create off-spec events." Here in the graph builder, these events are marked red. Obviously, this is important. It is a summary of chemicals that we put on a filter unit in the plant. I will show you the video soon. The second step is that in the partition, we came to the conclusion that above a certain solven t dosing, it is most probable or it is likely that we produce off-spec lots. There are two results from this evaluation. Don't put too much and be careful with the solven t dosing. Now I want to show you the video. During the this time, we sent a colleague to the plant and he took videos out of this reactor. The red rectangular is actually more or less the amount of what we put here on the filter unit. The green rectangular is the amount of solvent, which is dosed afterwards. If it's too few put on the filter, and too much solvent, obviously we created here a problem. After we discussed this with the team, it was about one -and -a -half years ago, we did not have these events anymore in the factory or in the plant. So for me, it was a quite successful story how we came here to the conclusion that we can be more careful in our plant about our processes. My primary result of this first part of my talk is that via the scripts and the way I imported data to JMP via the REST-API interface, it is possible to sharpen the awareness in the production with respect to deviations. Of course, there are more powerful regression possibilities in the tool which we are using, but it is not my job as a quality manager. The goal is to hand over here a powerful tool which we can use in our factory and plant for deviation management and root cause analysis. My idea is if someone is not wanting to use our tool JMP or wants to use a different tool, I can save always the scripts or the files as Excel tables. However, only the last three processes are available here, and it is limited because this is a project which was started 2017 to prepare all this data correlation and time between these three processes. However, what do I do if I'm interested in previous processes like the raw material or my first intermediate? How can I get this data in to jump from the outside of the plant? Therefore, I started to use and to investigate the interface and the possibilities to import data via a connection between Python and Azure. It started all when we had the manufacturing execution system changed by the end of 2020. The way I tried to go was stopped because this manufacturing execution system was stopped. I thought, "Was everything for nothing now?" No. I found a paper which was written about the synergies from JMP and JMP Pro with Python. The concept is shown here on the left side of the slide. In fact, I have a JMP script, I have a Python part, and I have an option in this Azure where I do my data access. Everything is also possible within seconds to get the data out of the plant. All is written here in the scripting guide, page 786. These are only a few pages. If questions come up, Emanuel from the JMP team helped me here to do this. Here's the concept. Let me move this Zoom picture. On the left part, it's what everybody in the chemical industry knows. Raw materials are delivered in trucks. They are stored in a tank farm and the production has to use raw material, of course. And raw material is consumed during the production processes and new raw materials are delivered and the tanks are refilled again. So we have a certain level and everything is fine. It starts and the way to investigate here and to get the data in to JMP is we have to look up our individual process of interest. Every point of interest in the plant has a certain number, and this number needs to be fined. So you can ask colleagues, you can ask SAP, you can ask the automation team, you can have a look by yourself. You need to find a way in your company who can help you. When you found the number, you have to make the request in Azure. This number I was talking about here, it's named at us, it's the unique ID. I type in here how many days back I want to see the data. I test it in Azure. He creates a table and I put this code into my JMP script and I click on execute. This is what I show you now. It's also done in few seconds only here. This first part you need to get from colleagues or you can look up in the internet. I looked up in the internet and this is not part of JMP support. Here you can see that within seconds I can have a look at the filling level of some of our raw material tanks or the tank farm. This is the concept that any process of interest can be imported like this into JMP within seconds. I wrote a manual or a journal how this can be done, and I will publish it also with my work in the community. If you don't know how to do it, it's written here. Where you can find it in the scripting index, I have a paper here how you test your system. I have a few recommendations for the help in the scripting index, and also I found some answers which I placed before in the community, so I hope to have also feedback from me documented there, and I showed you how to extract data from anywhere in the FAB or in the plant. And this brings me to the end of my talk. The summary is that product deviations happen to all of us. So here I show you some quality assurance tools, and via one click, I have all my necessary graphs and information prepared like this to discuss with the team. My message is that don't be afraid from using the JSL scripting language that I was before. When you start, you learn fast and you get the job done. You can prepare good discussions with your production teams and you can start continuous improvement plans and PDCAs, and everything can be available within a few seconds. I also have some ideas how to go on here, and there I need the help from you, so from the colleagues from which are attending the conference. Maybe you can help me automate and schedule my evaluations. I know that this can be done in JMP Live in version 17. If you don't have it, I need to use the Windows Scheduler. For example, I need to improve a little bit by creating add -ins, and the data cleaning still can be a little bit bit more better and improved, but okay. My main goal was to make the members talk more about suspicious events in our factory and out-of-control events, which is the lower right part of the picture of the slide, and less about off-spec events and out-of-control events. From quality management point of view, I really am a fan of turtle diagrams and turtle tools to document your improvements. This brings me to the end of my talk. I hope you enjoyed it as well as me when I was preparing this work, and I'm looking forward to your questions and to see you in Spain. Thank you.
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Using Generalized Regression To Analyze Designed Experiments With Detection Limited Responses (2023-EU-30MP-1308)
Saturday, March 4, 2023
Most measurement systems have detection limits above or below which one cannot accurately measure the quantity of interest. Although detection-limited responses are common in many application areas, such as the pharma, chemical, and consumer products industries, they are often ignored in the analysis. Ignoring detection limits biases in the results and even drastically lowers the power to detect active effects. Fortunately, the Custom Designer and Generalized Regression in JMP® make incorporating detection limits easy and automatic. In this presentation, we will use simulated versions of real designed experiments to show how to get the analysis right in JMP® Pro 17 and the pitfalls that will occur if detection limits are ignored in the analysis. We will also show how simple graphical tools can identify parts of the design region that could be problematic or even make it impossible to estimate certain model terms or interactions. Our examples will include an experiment designed to maximize the yield of a chemical product where the response is a reduction in the number of microorganisms in microbial susceptibility testing of consumer cleaning products. Hi, I'm Chris Gotwalt with JMP, and I'm presenting with Fangyi Luo of Procter & Gamble, and her colleague, Beatrice Blum, who'll be joining us for the in- person presentation at the Discovery Conference in Spain. Today, we are talking about how to model data from designed experiments when the response is detection limited. This is an important topic because on the one hand, detection limits are very common, especially in industries that do a lot of chemistry, like the pharmaceutical and consumer products industries. While on the other hand, the consequences of ignoring detection limits leads to seriously inaccurate conclusions that will not generalize. This leads to lost R&D time and inefficient use of resources. The good news that we are here to show today is that getting the analysis right is trivially easy if you are using generalized regression in JMP Pro and know how to set up the detection limits column property. In this talk, we're going to give a brief introduction to sensor data, explaining what it is, what it looks like in histograms and a brief description of how you analyze it a little bit differently. Then Fangyi is going to go into the analysis of some designed experiments from Procter & Gamble. Then I'm going to go through an analysis of a larger data set than the one that Fangyi introduced. Then we're going to wrap up with a summary and some conclusions. What are detection limits? Detection limits are when the measurement system is unable to measure, at least reliably, when the actual value is above or below a particular value. If the actual value is, say, above an upper detection limit, the measured value will be observed as being at that limit. For example, if a speedometer in a vehicle only goes to 180 kilometres an hour, but you are driving 200 kilometres an hour, then the speedometer will just read 180 kilometres an hour. In the graphs above, we see another example. We see five histograms of the same data. The true or actual values are over here on the left, and moving to the right, we see what results when you apply an increasing detection limit to this data. What happens is we see this characteristic bunching at the detection limit. When you see this pattern, it's a really good sign that you may need to think about taking detection limits into account in your distributional or regression analysis. Why should we care about detection limits in a data analysis? Well, if you don't take your detection limits into account properly, you'll end up with very heavily biased results, and this leads to very poor model generalization. The regression coefficients will be way off. You'll have an incorrect response surface, which leads to matched targets with the Profiler being way off. I think the situation is a little bit less dire when maximizing a response, but there's still quite a lot of opportunity for things to go wrong. In particular, Sigma, your variance estimate will still be way off, which leads to much lower power, you have completely unreliable p- values. The tendency is that variable selection methods will heavily under select important factors . The actual impact that a factor has on your response will be dramatically understated if you don't take the detection limits into account. The two tables of parameter estimates that we see here illustrate this very nicely. On the left are the parameter estimates from a detection- limited LogN ormal analysis of a regression problem. On the right, they are the resulting parameter estimates when we ignore the detection limit. We see that the model on the left is a lot richer and that a lot of our main effects, interactions, and quadratic terms have been admitted into the model. Whereas on the right, when we ignore the detection limit, we're only able to get one main effect and its quadratic term included in the model, and the quadratic term is heavily overstated with a value of negative 11.5 about relative to the value in the proper analysis where that quadratic term is equal to just negative 3. We see that we're really missing it on a lot of the other parameters here as well. When we take a look at this in the Profiler, this becomes really apparent. O n the left, we have the Profiler of the model correctly analyzing with the Limit of Detection, and we see that all the factors are there, and overall, the response surface is pretty rich- looking. On the right, we see that only the one factor, dichloromethane, has been included in the model. T he solution to the problem that you would get with the problem on the left is likely rather different from the one that you would get on the right. Thanks, Chris. Now I'm going to share with you a little bit background on the experiment of the data mentioned by Chris, the time to bacterial detection. The objective of that experiment was to understand hostility impacts of our formulation ingredients or factors on a liquid consumer cleaning formulation. The experiment was a micro- hostility Design of Experiment with 36 samples, and we have five key formulation factors A, B, C, D, E. W e have two responses from this experiment. They're from microbial testing. The first one was the one mentioned by Chris. It is time to bacteria detection in two days, and it was measured by hour. I f we are not able to detect the bacteria in two days, then time to bacteria detection is right censored at 48 hours. So the Limit of Detection for this endpoint is 48 hours. Another endpoint is log reduction in mode from Micro Susceptibility Testing. For this endpoint, what we did is that we add certain amount of mold to the formulation, wait for two weeks, and measure amount of mold in the product after two weeks. T hen we calculate the reduction in log base time mold, and this is the second endpoint. Limit of Detection for this endpoint is six unit. T his shows you the detailed data from the experiment, the first 15 samples. Y ou can see the formulation factors A, B, C, D, E, and they were from response surface design. W e have two endpoints, the bacteria detection time in hours and the log reduction in mold. The data highlighted in red are right censored data. We can use histograms and scatterplots to visualize our data as well as factor versus censoring relationship. As you can see from the histogram, more than 50 % of samples are right censored at 48 hours. If an observation is not censored, then most of them will be below 15 hours. O n the right, we have the scatter plot. The red circle indicates the censored data points. You can see that we have censoring at all levels of the factors except for factor C. We don't have the censoring at higher level of C, but we observe censoring at all level of the factors. In JMP Pro 16 and higher, we can specify column properties for detection limit. W hen you go to column property, you find detection limits, and then you can specify the lower detection limit and upper detection limit. I f a data point is below the lower detection limit, that means it's less censored at the lower detection limit. If a data point is higher than the upper detection limit, then it means that it's right censored at the upper detection limit. For the bacterial detection time, we have an upper detection limit and it's 48 hours. W e put 48 hours in the upper detection limit box. After we specified detection limit on the column property in JMP, then we can use JMP generalized regression modeling to analyze the data by taking into account the Limit of Detection. So this is a new feature in JMP Pro 16 and higher. For this type of analysis, we need to first specify the distribution for a response and estimation method. W e try different distribution for the data and use the forward selection method, and we found Normal distribution fits the data the best because it has lowest AICc. We can also analyze data ignoring the detection limit. Y ou can see that we will have a much smaller model with five factors left in the final model. T he model ignoring Limit of Detection will have much less power to detect significant factors. This showed you the factors left in the final model from the generalized regression modeling. If we take into account Limit of Detection for the response, or if we ignore Limit of Detection in the response. As you can see, if we take into account Limit of Detection, then we have much more significant factors in the model. W e can only detect the effect of C and D and their quadratic effect in the model if we ignore Limit of Detection for our response. Again, this is comparison of the parameter estimate from the model if we consider Limit of Detection in the modeling or ignoring Limit of Detection in the modeling. Ignoring Limit of Detection in the modeling would give us the bias estimate of the parameter as well. This slide shows you the prediction Profiler of the response if we perform the modeling by considering the Limit of Detection versus ignoring the Limit of Detection. If we consider the Limit of Detection in the modeling, then we get a model with all the terms in the model, the main effects as well as some of the interaction and quadratic terms. T his model makes much more sense to our collaborators. Remember that at lower level of C and at higher level of D, we have more censoring data. That means the detection time is longer and the prediction Profiler showed that at lower level of C and a higher level of D, the predicted detection time is longer. A lso because we have more censored data in those region, so the confidence interval for the prediction P rofiler is wider. If we ignore the Limit of Detection in the analysis, we get much less significant factors. Only C and D showed up in the model, and the parameter estimate is also biased. This one shows you the diagnostic plotting of observed data on the y- axis versus predicted data on the x- axis. If we consider Limit of Detection in the generalized regression modeling, it gives correct prediction. But if we ignore Limit of Detection in the modeling, then it will give incorrect prediction for your data. In addition to the prediction Profiler, JMP generalized regression modeling would also give you two profilers similar to those from Parametric Survival M odeling platform. Those are the Distribution Profiler and Quantile Profiler. The distribution profiler will give you the failure probability at a certain combination of our formulation factors and a certain detection time. The Quantile Profiler will give you the quantile of the detection time at a certain combination of our formulation factors and the specified failure probability. T hese two profilers are available in JMP under the Generalized Regression Modeling. But one advantage of using Generalized R egression Modeling to analyze time to failure type of data is that it would provide you the Prediction Profiler, and this type of profiler is much more easier for our collaborator to understand. I t's much harder to explain the Distribution Profiler and Quantile Profiler to our collaborators. Now it comes to the analysis of the second endpoint, the log reduction in mold. Again, we can use histogram and the scatterplot to visualize our data and visualize the factor versus censoring relationship. As you can see from the left histogram, you can see that we have a lot of data that are right censored at six unit. We can see censoring at all level of our formulation factors, except at higher level of C and lower level of E. T his is the region of concern. We have seen a lot of censoring at lower level of C and higher level of E. That means at lower level of C and higher level of E, it's good for the product. We have higher log mold reduction. Again, we can use detection limit on the column property to specify the Limit of Detection for this endpoint. W e used upper detection limit of six in this column property. N ow the next step is to analyze this data using the Generalized R egression modeling by taking into account the Limit of Detection. W e use LogN ormal distribution and forward selection. Interestingly, we found that the RS quare is one and this is very suspicious. A lso, we see some red flag. The AICc had a severe drop after step 17. T he standard error of the estimate as well as the estimate for the scale parameter seems to be extremely small. A lso, the diagnostic plot showed perfect prediction from the model. W e know that the model has overfit. This is the Prediction Profiler, and they showed very narrow confidence interval for the prediction, and we knew that our model is overfit. So what we did for the modeling is that we tried a simpler model by removing the quadratic terms from the initial response surface model. We found that LogN ormal with forward selection model fits the data the best because it has a lowest AICc and BIC. T his time, the solution path looks more reasonable as well as the standard error estimate of our parameters and estimate of the scale parameter of the LogN ormal distribution. T he diagnostic plot looks more reasonable now. This is the Prediction Profiler of the final model after we removed the quadratic terms. This Prediction Profiler makes a lot more sense. Recall that at lower level of C and at higher level of E, we have more censored data you can see here. That means at lower level of C and higher level of E, we have higher log mold reduction. It showed on the Prediction Profiler because it has more censored data in this region and the confidence interval for the prediction is wider. We can also compare the final model Prediction Profiler if we ignore Limit of Detection in the modeling. If we ignore Limit of Detection in the modeling, we got less significant model factors as well as biased results. If we ignore Limit of Detection in the Generalized Regression modeling, then the second model, which is incorrect and is trying to use the quadratic term to predict in the lower level of C and higher level of E. So t rying to get the predictive value close to the Limit of Detection, and we knew that this result is biased. Fangyi has nicely shown here that the incorrect analysis, ignoring the Limit of Detection, leads to some seriously biased results. And that getting the analysis right is easy if you set up the detection limits in either the custom designer or as a column property. I'm going to go through one more example that has measurements at different times, which adds a little bit more complexity to the model set up, and in our case, required some table manipulation to get the data in the right format. Here is the data table of the second DOE in basically the form that it originally came to us. In this data, we have 8 factors, A through H, and the data has measurements at 1 day, 2 days, and 7 days. Originally, our intent was to analyze the 3 days separately, but when we fit the day 7 data, the confidence intervals on the predictions were huge. It was apparent that there was so much censoring that we were unable to fit the model, and so we were either going to have to come up with another strategy or back away from some of our modeling goals. What we ended up doing was we used a stack operation from under the tables menu so that the responses from different days would be combined together into a single column, and we added day as a column that we could use as a regression term. In the histogram of log reduction, we see the characteristic bunching at the detection limit of five. Combining the data like this certainly seems to have improved the impact of censoring on the design and hopefully allows us to make more effective use of all the data that we have. As in the previous examples, we start off fitting a full RSM model, but in this case, because we have day as a term, we add a day and interact all of the RSM terms with day in the Fit Model Launch Dialog prior to bringing up the generalized regression platform. Again, we're going to use the LogN ormal distribution as our initial response distribution. Because this is a large model, we can't use best subset selection, so we used pruned forward selection as our model selection criterion. We try the LogN ormal, Gamma, and Normal distributions, and clearly the LogN ormal comes out as the best distribution because its A ICc is 205.3, which is more than 10 less than the second best distribution, which was the Normal, whose A ICc was 257. Here, the model fit looks really reasonable with nothing suspicious. The solution path standard errors, scale parameter, and the actual by- predicted plots all look pretty good and realistic. There's a little bit of bunching down at the low end of the responses, but the thinking is that wasn't due to a detection limit, just a part of the discreetness of the measurement system at lower levels of reduction. Now, if we repeat this analysis, ignoring the detection limit, it guides us towards the normal distribution. Here we see the Profilers for the model that incorporated the detection limit on the top and the model that ignored the detection limit on the bottom. As in the other examples, we see that the size of the effects are dramatically muted when we ignore the detection limit and we get quite a different story as there's a strong relationship between log reduction in factor E when we take the detection limit into account properly, and that effect is seriously muted when we ignore the detection limit. If we compare the actual by- predicted plots for the two models, the model with the Limit of Detection taken to account properly is tighter around the 45- degree line for the uncensored observations. W e see that the model ignoring the detection limit is just generally less accurate as the observations are more spread out across the 45- degree line. Those are our two case studies. In summary, I want to reiterate that detection limits are very common in comical and biological studies. As we've seen in our case studies, ignoring detection limits introduces severe model biases. T he most important message is that using the column property or setting up the detection limits in the custom designer make analyzing detection- limited data much easier to get correct. There are some pitfalls to watch out for in that if you see standard errors that are unrealistically small, or models are unrealistically accurate, you may need to back off from the quadratic terms or possibly even interaction terms. We've shown how histograms can be used to identify when we have a detection limit situation. It's useful to see the censoring relationship between different factors, because if there are big corners of the factor space where all the observations are missing, then we may not be able to fit interactions in that region of the design space. A gain, if the model looks too good to be true, go ahead and try a simpler model, back off a bit. That's all we have for you today. I want to thank you for your attention.
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Pack, Stack, and Response Screening Hacks: How JMP® Clinical Uses New JMP® 17 Features (2023-EU-30MP-1285)
Saturday, March 4, 2023
With version 17, JMP Clinical is now a fully JSL implemented product. This presentation will demonstrate the reimagined JMP Clinical and how it uses new JMP 17 features. Three new features in Tabulate format the tables produced by clinical reports to be publication-ready. Pack combines counts and percentages (or other statistics) into one column, while stack allows multiple grouping variables to be combined into one column. Tables displaying event counts also take advantage of the new Unique ID feature in Tabulate to count events only once per subject identifier. With these three new features, tables can be copied and pasted into any publication or report. JMP Clinical’s risk reports also use JMP’s new Response Screening platform to identify safety signals by calculating risk difference, relative risk, and odds ratio faster than previous versions. With all these new JMP features, JMP Clinical produces publication-ready reports quickly and effectively. Hi. Thank you for joining me today. My name is Rebecca Lyzinski. I'm a senior software developer for JMP Statistical discovery. Today I'll be talking about how JMP Clinical uses some of the new JMP 17 features, such as Pack, Stack, and Response Screening. First I'll talk a little bit about what JMP Clinical is. Then I'll go into what changes have occurred in JMP Clinical 17 compared to previous versions, and then show a demo of JMP Clinical and how it uses the new tabulate features of Stack, Pack and unique IDs, as well as the new Response Screening platform. First, what is JMP C linical? JMP Clinical is a JMP product that is used to analyze clinical trial data. It works by using the standard formats of CDISC, SDTM and Atom data. Once the data is loaded, JMP Clinical runs interactive reports for events, findings, interventions and more. JMP Clinical is used by a variety of fields, including medical doctors, medical writers, clinical operations, and statisticians. In addition, JMP Clinical works with JMP Live to share reports across your organization. With JMP Clinical 17, there's a big change, in that JMP Clinical no longer uses SAS as the basis for the code underlying the reports. Starting with JMP Clinical 17, it is now completely built off of JMP. This means that we have a faster installation because the installer is now more compact than it was before. JMP Clinical 17 also has all of its reports redesigned using JSL as the underlying code system for the reports. Another change is that now the reports will auto run. There's no longer a need to click a button in order to get the report to run. JMP Clinical 17 will also include some new reports, including the FDA Medical Queries and the Algorithmic FDA Medical Queries? One additional change is that now all the study preferences are in one location. You only have to go to one place to change a preference, and it will take effect across all of your reports. Now I'm going to switch over to JMP Clinical for a quick demo. When you first open JMP Clinical, a main window will appear with three different tabs one for Studies, one for Reviews, and one for Settings. The Studies tab is where all your study data is located. Here you'll see that I have the study, the Nicardipine loaded. You'll see paths for the SDTM and Atom locations of your data, as well as which domains from those folders have been loaded for the study. This is also where you can add a new study. You can refresh the study metadata for an existing study. If you add data to it, or you add variables, or you change variable names, you can refresh the metadata and all those changes will take effect. You can also set study preferences or set the value order in color for a given study f rom this tab. Set study preferences is new in JMP Clinical 17. It will open a new dialog. Here you can change any of these widgets and the new values will take effect across all of your reports. F or example, if you didn't want your reports to run off the safety population and you wanted them to run on all subjects instead, you can change to all subjects here. Once you click Okay, all your reports will now run off of all subjects instead of the safety population. The next tab is for Reviews. Here, when you click Start New Review, the Review Builder will open and you'll be able to select which reports you want to see. For this example, I'm going to look at the demographics, distribution AE D istribution, AE Risk Report and the two FDA medical query reports. If you wanted to add additional reports, you can click on Add Report. A new window will open up with all the possible reports you can run on this study, and you can make additional selections. Demographics distribution is usually a good place to start in any clinical trial. Here there are tables and distributions for each demographic characteristics such as sex, race and age. Tabulate is used to create the tables at the top, and you can see here that the Counts and Percents are combined into one column using Tabulate's new feature of Packed Columns. Underneath is a distribution for each of the demographic characteristics. On the side, there's an option to add additional distributions if there are other characteristics you would like to see. By clicking the Add button, you can add any variable from either the ADSL or DM data set, and it will show up under Distributions. There's also an option to perform treatment comparison analyses. When this button is clicked, the report will automatically rerun. Now at the bottom of the report, there's a one way analysis for age and a contingency analysis for sex and race. This allows for comparisons between treatment groups to be done to see if there are any differences between the treatment groups. Typically, an important safety analysis that occurs in any clinical trial is to analyze the adverse events that occur throughout the trial. In Adverse events distribution, there's a graph and a table showing the distribution of adverse events across treatment groups. At the top is a bar chart with the count of adverse events split out by NiNicardipine and Placebo, the two different treatment groups for the NiNicardipine study, they're shown in descending order for each treatment group. Under the graph is a tabulate. Here, you'll see that the first column is body system organ, class and dictionary drive term. These are two different measure terms that are used to classify adverse events, and they're being stacked on top of each other in the tabulate. In the other columns are Counts and Percent split out by the planned treatment group, as well as a total count and Percent. This table uses a lot of the new JMP 17 features for Tabulate. The first one is the Stack Grouping Columns. Here you can see if you right- click on the Column, the Stack Grouping Columns option is checked. If we were to uncheck it, it gets split back out into two separate columns. This is how Tabulate works for JMP Clinical 8.1 in previous versions where we had to have two separate columns for the two different variables. Now, by selecting both columns and right clicking and going to Stack Grouping Columns, we can combine them back into one column. This allows the table to now be publication ready for any PowerPoint or journal article that it might want to be used in. Somewhat similarly, we have the Count and Percent in one column which did not exist before. If you right- click on one of these columns, you'll see the new Pack Columns option. If we unpack the columns, they're now separate into two columns, one for the Count and one for the Percent. By selecting both columns and right- clicking and going to Pack Columns, we can now pack them back into one column so that the Count and Percent show up together. The other option that this table uses is if you open up the control panel from the red triangle, you'll see that there's an ID variable that's been added that didn't exist before. Here you'll see that unique subject Identifier has been entered as the ID variable to use in this table. What that option does is it counts each subject only once on each row of the table. For example, if the subject had both a vasoconstriction event and a hypertension event, they would only get counted once with in vascular disorders. Previously, before the ID variable existed, this Vascular disorders row would have been a sum of all of the events that happened underneath it, which may overestimate the number of subjects that had a vascular disorder event. You can also see at the bottom of the table that this option now adds a row called all. What this represents is the number of subjects with any adverse event. That's another nice additional feature added through the ID variable. With these three changes, we now have a very nice publication ready table to print out to whatever word document PowerPoint you want to include it in. A couple of other features to mention on this report before moving on to the next one is that there are some options listed under Data. For example, if you wanted to look at a different measure term than the ones that are automatically presented, you can change them here to report a term, Highlevel Term, etc . You can also change the report to run on pretreatment events, treatment events, on- treatment or off- treatment events. The Demographic Grouping Widget will change out the variable on the y axis of the graph builder, as well as change the variable used in the Tabulate to whichever variable is selected from demographic grouping. There's also an option to Stack both the table and the graph. For example, if you wanted to see the adverse events split out by severity, we can select severity. Now the bar chart is stacked by mild, moderate and severe events. The table is also split out into columns for mild, moderate and severe. The report also uses a local data filter in order to filter both bar chart and the tabulate. You can filter on things such as whether or not the event is serious, whether or not the event is related to the study treatment. We can also filter on a correct overall percent occurrence of the adverse events. For example, if we only wanted to see adverse events that occur in 5% or more of the population, we can change this filter. Now the bar chart and the table are both filtered down to only subjects, only adverse events that have at least a 5% occurrence in the population. Another way to analyze adverse events is through the Risk Report. This risk report uses the new JMP 17 Response Screening Platform to create both a Risk Plat and a Tabulate. The Risk Plat shows the percent occurrence of subjects within both treatment groups, so Placebo and a Nicardipine, and it also shows the risk difference in comparing the Nicardipine to Placebo along with a 95% confidence interval. The table repeats this information just in tabular form with columns for the Counts and Percent in each treatment group, as well as a column for the risk difference in the 95% confidence interval. The Response Screening platform works off a table that looks like this one, where we have unique subject identifier as the first column, and then there's a column for each adverse event. That's an indicator column with zero representing no event and one representing an event. If we pop out this table. The Response Screening platform is located under Analyze Screening. Response Screening. It will open up a new dialog where you can select your variables that you want to compare. Because there are 202 different adverse event columns , we've combined them into a group of columns and this allows you to just select one variable and it will automatically put all 202 columns into the Y Response Column using Plan Treatment for our X and click Okay. Response Screening then brings up this window. The default view is to look at the FDRP values and a table of those values. JMP Clinical uses the two- by- M results table. This is where Response Screening calculates the relative risk, risk difference and odds ratio. JMP Clinical works by creating making this table into a data table and then using Graph Builder and Tabulate to format it in the view that was shown in the report. In order to get the additional columns needed, if you right-click on the table and go to Columns, you can select the different 95% confidence interval variables as well as a total count and the different counts for the positive versus negative comparisons. Once that Response Screening is run, then it's created into a data table in this bar chart and the tabulate are created. The tabulate again uses the Pack columns option to put Counts and Percent into one column, but it also uses it to put the risk difference in 95% confidence interval into one column. If we were to unpack this group of columns, you would see that it originally started as three different columns. Even with three different columns, we can still pack them together into one column. If you didn't like the format of the way that they automatically packed together, you can right- click on the column, go to Pack Columns and Template. Here you can change the format of how the column appears. For example, if you wanted to see brackets instead of parentheses, you could change them here. You could also change how the columns are delimited. The default is a comma, but you could use a semicolon or any other character that you wanted to separate out your columns. Similar to the AE Distribution Report, this report has a few different options. Some that are different are that you can change the risk measurement, so you can look at either risk difference, relative risk, or odds ratio. You can also display the risk difference as either a percent or a proportion, and you can sort the plot in the tables by risk measurement count or alphabetically. This report again uses a local data filter to filter both the plot and the table by either a dictionary drive term, the risk difference, or the absolute risk difference. Here you can see that I filtered the risk difference down to two or greater so that we can see the Plot and Table a little more clearly. Another view of the Risk Plot and the Response Screening output is the FDA Medical Query Risk Report. This starts out as just being called Medical Query Risk Report, and then there's an option to analyze it either by FDA Medical Queries or standardized medical queries. Medical Queries are a way to group adverse events into different medical conditions, and these are the two different standards. Standardized Medical Queries are created by MedDRA and usually come as SD files. In September of 2022, the FDA released their own Medical Queries as an Excel file that can be downloaded from the web. JMP Clinical handles both of these different standards and can be switched on this report back and forth by selecting either FDA Medical Queries or Standardized Medical Queries. Just like on the AE Risk Report, there is a risk plot with the percent occurrence for each treatment group and the risk difference between the Nicardipine and Placebo. The difference is that on this report, the Risk Plot is split out by scope. Either a broad medical query or a narrow medical query. Underneath some custom scripting is used to create tables that stack the medical queries by the preferred terms that contribute to them. Just like in the AE Risk Report, we have counts for columns for the Counts and Percents, as well as a column for the risk difference between Nicardipine and Placebo. Here you can see that, for example, for Arrhythmia, the dictionary derived terms that contribute to that medical query are Atrial, Flutter, Atrial f ibrillation, Arrhythmia, Bradycardia and a few others. Underneath that table is the same table just for broad medical queries split out by preferred terms, a table for medical queries split out by broad or narrow, depending on the scope, and a table for which medical queries are contained in each system organ class. For example, gastrointestinal disorders is made up of abdominal pain. The last report I'm going to show is a brand new one in JMP Clinical 17.1 inversions beyond that. Within the FDA medical query Excel file, there are some text boxes for different algorithms in a few different medical queries. The algorithms include criteria that's not just limited to adverse events. For example, in H yperglycemia, a subject could be categorized as having Hyperglycemia if they have an adverse event that falls into the Hypergysemia FMQ category. But they also could be classified as having Hyperglycemia if within the lab data set, they have more than two plasma glucose values over 180 milligrams per deciliter. This report uses the adverse event data set, the lab data set, and the continent medications data set to determine if subjects have a given medical query, rather than just looking at the adverse events and mapping them to a medical query. Similar to the other risk reports, this report uses a local data filter to allow you to filter on the medical queries the risk difference and the absolute risk difference. Again, we have the same options to switch between event type, the risk measurement for risk difference, relative risk, or odds ratio, and for sorting the table by risk measurement, count or alphabetically. That was a quick overview of some of the JMP Clinical features and how JMP Clinical uses the new JMP 17 features in Tabulate and Response Screening to make our reports. However, JMP Clinical is a much bigger product than just those five reports. We actually have over 30 interactive reports. Some commonly used ones that I didn't mention are the Adverse Event Narratives, The Patient Profiles, A Study Flow Diagram, like the figure below, that shows you how subjects progress throughout the study and the ability to analyze by high's law cases. JMP Clinical also works with JMP Live. At the top of each report there's a button that if you click it, it will publish and share the report across your organization. There are also future features coming in 17.1 and future versions, such as adding the ability for crossover support, for analyzing crossover studies. There'll be even more reports being added, such as a couple of oncology reports. Thank you so much for your time. I would appreciate any comments or feedback if you want to leave them or email me directly. Again, thank you for your time and hope you have a wonderful day.
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Predictive Models for (BCO) Lameness Using Health, Stress, and Leg Health Parameters in Broilers (2023-EU-PO-1342)
Saturday, March 4, 2023
ABSTRACT Stress and lameness negatively affect the health, production, and welfare of animals. The following physiological and non-invasive measures of stress and lameness were measured: core body temperature, corticosterone (CORT) concentrations in serum and feathers, surface temperatures of the head (eye and beak) and legs (hock, shank, and foot) regions by infrared thermography (IRT), leg blood and oxygen saturation (leg O 2 ). JMP Pro 17 Model Screening platform was used to fit several parametric and machine learning models to the binary response variable (Lame=1) of the 256 study birds on the nine health and stress indicators mentioned above. We selected K Fold Cross-validation with K=5 and repeated the process twice (N Trials Folds=10). The best models were the Neural Boosted (Mean AUC=.985 and misclassification rate of zero in 50 validation birds) and the Generalized Regression Lasso (Mean AUC=.975 and misclassification rate of 3 in 50 validation birds). The Stepwise Logistic Regression and most interesting for explaining BCO required only seven of the nine indicators and had a similar overall fit performance as the other two. Both REG models “agreed on the significant predictor effects,” and when applying Model Comparison to compare them further found them nonsignificant to each other (comparing their AUC’s). Hello, everyone, and welcome to our presentation. My name is Dr Andy Mauromoustakos. I'm a professor at the Agricultural Statistics Lab at the University of Arkansas. My co- presenter is Dr Shawna Weimer . She's A ssistant Professor at Poultry Science Department at the University of Arkansas, and she's the Animal Welfare chairperson at the university. We're going to talk to you today about predictive models for BCO lameness using health and stress and leg health parameters in broilers. Our presentation is going to be short. We're going to discuss the models that we are fitting, the champion models, the ones that they get the medals. We're going to evaluate them, and we're going to have some conclusions in the end. Shawna? All right. This study compared physiological and non-invasive measures of stress and lameness in clinically healthy and BCO— or b acterial chondronecrosis with osteomyelitis—laden broilers. BCO is a leading cause of infectious lameness in broiler chickens, with flock diagnosis requiring euthanasia. Thus, there's a need for technological innovations to detect health and lameness status in animals to project the disease likelihood prior to clinical onset. In this study, birds were raised in separate environmental chambers with either wood shavings on the floor, or the litter, and a wire flooring model that is validated to induce BCO lameness. Nine non-invasive measures of stress and lameness were collected from 256 birds, male broilers, over several weeks, which included core body temperature, stress hormone, surface temperatures of the head, the eye, and the beak, and the legs, the hocks, the shank, and the feet, with infrared thermography. Leg blood oxygen saturation was also measured with a pulse oximeter. Of these measures, two we sought to validate. The first was extraction of corticosterone from the feathers. Corticosterone is the major primary stress hormone, and the gold standard for measures is blood serum concentrations, which requires the capture and restraint of the bird to collect it. So if feather corticosterone could be validated, then we could simply clip a feather and not put the bird through that stress of restraint and blood draw. The second was the thermal images, of which each pixel has its own temperature recorded and can be used to quantify external changes in skin temperature related to blood flow, offering a non-invasive tool to measure health and welfare. During stress, peripheral blood is shunted to the core, and we expected the average pixels of the eye and the beak, or Eavg and Bavg, to be lower in lame than sound birds, which we correlated with the serum corticosterone. For the thermal images of the legs, we expected the average pixel temperature of the hock, the shank, and the foot to be lower in the lame birds, both for the stress reasons and also for the colonization of the bacteria that slowed the blood flow, which we correlated with the leg blood oxygen saturation. There were marked differences between lame and sound birds. Our objectives is to identify which of these nine health and stress indicators are important for lameness. We want to build models, both for prediction purposes, but we are in agriculture, and we like to publish papers that try to explain how our inputs are affect the response. We are hoping that some of the models, the traditional regression models, will do fairly well and we can interpret those. In our methods, we talked about… It's a balanced study. It's a match paired experiment where every time a sound bird is observed, a lame bird is also observed in the same indicators. We have an incidence of a disease of 0.5. We're going to take advantage of the Model Screening platform of JMP Pro 17. We're going to select the lame that has values 1 and 0 categorical. 1 stands for, yes, it's lame. 0 is the sound . We have our nine predictors in here. We're going to do the defaults. We're going to fit all of the machine learning models, that they are checked. We're going to select cross validation. We're going to have 5-fold cross validation. With our approximate 250 birds, we're going to have about 50 birds per fold, and we're going to repeat it twice. We selected the random seed so we can reproduce the results. Notice that I did not add the quadratic terms and interaction terms to hopefully have easier interpretations. When we select this, and we click " Run ," JMP takes about a minute. I t's going to come up with the ranking of the models that we have fitted. This is what we call the beginning of the end. We have 10 different data sets that we have tried. This is the average fit criteria, higher the RS quare, and is the ranking the best, second best, third best. Hopefully for us, we expected that the Neural Boosted model may do a little bit better than the traditional regression models, such as the Penalized Regression and the Logistic Regression models, but we were happy to see that these are our close second. If we wanted to see how our best model did, we're going to go and see that the best model— the Neural Boosted, that is the best model for predicting purposes —had a misclassification of 0. You can see here both in the training and the validation that our receiver operating characteristic curve reaches very soon the 1 and stays at 1, which is extremely good. We see the confusion metrics that out of the 49 birds, we're not really misclassifying any of them, so this is a very good model with a very high generalized RS quare and all of the other fit criteria that is produced in here. But we are more interested in the regression type of models. The regression type of models, the Generalized Regression, we can see that when we use Lasso as the estimation method, the model included all of the variables, and we have couple of non -significant variables in the model in here. We can see these non -significant factors, such as the FCORT, you can see it that it's not crossing the horizontal line. You can see that the year average is not significant, but is included. We can see that this model had approximately three misclassification, a misclassification rate of about 7 %. Here is the third model that was... If we had to give it gold, silver, and bronze, these two will share the second place. The Logistic Regression model, when we did the step wise procedure, decided not to include the two highly non -significant factors. We can see in our regression model, in the Logistic Regression model, that SCORT is the most important variable. This model has similar misclassification rate of three, the same as the Logistic Regression. Here is how the seven indicator variables are used to predict the probability of lameness. What we like about the regression type of models is that we can get odds ratios that will help us to interpret. For example, for our most important variable, we can see that the odds of lameness is twice with one unit of increase is serum cortisol. Overall, we would like to say that the Logistic Regression model only used seven out of the nine indicators. The Neural Boosted model and the Generalized Regression, both of them used all nine indicators for lameness. All of the models have area under the curve of greater than 0.9. All of the models have a lower… The regression type of models had the 7 % misclassification on the validation set, and the Neural Boosted did not have anything. We can compare our three winner models using the Model Comparison platform in JM P. We can see that in terms of predicting, strictly predicting, the Neural Boosted model is significantly better than both of the Generalized Regression and the regression model. The two regression models , the one with all nine variables versus the one with the seven variables, are not significantly different from each other. They all had a very similar area under the curve, and they had similar misclassification of three birds. This is our presentation. We'd like to thank you for your attention. We have some references that you can find similar related material to the techniques that we used in JMP documentation that will help you through this process. Thank you for your attention.
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