Slides from Advanced Mastering JMP webinar using JMP Genomics V 10.   Other Resources:   An integrated clinico-metabolomic model improves prediction of death in sepsis case study Related JMP success story  describing use of JMP Genomics for the vast majority of all analyses   ... View more

Level: Intermediate   Kelci Miclaus, Senior Manager Advanced Analytics R&D, JMP Life Sciences, SAS   Reporting, tracking and analyzing adverse events occurring to patients is critical in the safety assessment of a clinical trial. More and more, pharmaceutical companies and the regulatory agencies to whom they submit new drug applications are using JMP Clinical to help in this assessment. Typical biometric analysis programming teams may create pages and pages of static tables, listings and figures for medical monitors and reviewers. This leads to inefficiencies when the doctors that understand medical impacts of the occurrence of certain events can not directly interact with adverse event summaries. Yet even simple count and frequency distributions of adverse events are not always so simple to create. In this presentation we focus on key reports in JMP Clinical to compute adverse event counts, frequencies, incidence, incidence rates and time to event occurrence. The out of the box reports in JMP Clinical allow fully dynamic adverse event analysis to look easy even while performing complex computations that rely heavily on JMP formulas, data filters, custom-scripted column switchers and virtually joined tables.      Auto-generated transcript...   Speaker Transcript Kelci J. Miclaus Hello and welcome to JMP Discovery Online. Today I'll be talking about summarizing adverse event summaries and clinical trial analysis. I am the Senior Manager in the advanced analytics group for the JMP Life Sciences division here at SAS, and we work heavily with customers using genomic and clinical data in their research. So before I go through the summarizing and the details around using JMP with adverse event analyses, I want to introduce the JMP Clinical software which our team creates. JMP Clinical is one of the family of products that includes now five official products as well as add ins, which can extend JMP to really allow you to have as many types of vertical applications or extensions of JMP as you want. My development team supports JMP Genomics and JMP Clinical. JMP Genomics and JMP Clinical are respectively vertical applications that are customized, built on top of JMP, that are used for genomic research and clinical trial research. And today I'll be talking about how we've created reviews and analyses in JMP Clinical for pharmaceutical industries that are doing clinical trials safety and early efficacy analysis. The original purpose of JMP Clinical and the instigation of this product actually came through assistance to the FDA, which is a heavy JMP user And their CDER group, the Center for Drug Evaluation and Research. Their medical reviewers were commonly using JMP to help review drugs submissions. And they love it. They're very accomplished with it. One of the things they found though is that certain repetitive actions, especially on very standard clinical data could be pretty painful. Example here is the idea of something called a shift plot which is for laboratory measurements where you compare the trial average of a laboratory of versus the baseline against treatment groups. In order to create this, it took at least eight to 10 steps within the JMP interface of opening up the data, normalizing the data, subsetting it out into baseline versus trial, doing statistics, respectively, for those groups merging it back in, then splitting that data by lab tests so you could make this type of plot for each lab. And that's not even to get to the number of steps within Graph Builder to build it. So JMP clearly can do it, but what we wanted to do is solve their pain at this very standard type of clinical data with a one-click lab shift plots, for example. In fact, we wanted to create clinical reviews in our infrastructure that we call the review builder that are one-click standardized reproducible reviews for many of the highly common standard analyses and visualizations that are required or expected in clinical trial research to evaluate drug safety and efficacy. So JMP Clinical has evolved since that first instigation of creating a custom application for a shift plot into a full-service clinical...clinical trial analysis software that covers medical monitoring and clinical data science, medical writing teams, biometrics and biostatistics, as well as data management around the study data involved with clinical trial collection. This goes for both safety and efficacy but also operational integrity or operational anomalies that might be found in the collection of clinical data as well. Some of the key features around JMP Clinical that we find to be especially useful for those that are using the JMP interface for any types of analyses are things like virtual joins. So we have an idea of a global review subject filter, which I'll show you during the demonstrations for adverse events, that really allow you to integrate and link the demography information or the demographics about our subjects on a clinical trial to all of the clinical domain data that's collected. And this architecture, which is enabled by virtual joins within the JMP interface with row state synchronization, allow you to really have instantaneous interactive reviews with very little to no data manipulation across all the types of analyses you might be doing in a clinical trial data analysis. Another new feature we've added to the software that also leverages some of the power of the JMP data filter, as well as creation of JMP indicator columns, is this ability to, while you're interactively reviewing clinical trial data, find interesting signals that say, in this example, the screenshot shown is subjects that had a serious adverse event while on the clinical trial, find those interesting signals, and quite immediately, create an indicator flag that is stored in metadata with your study in JMP Clinical that's available for all other types of analyses you might do. So you can say, I want to look now at my laboratory results for patients that had a serious adverse event versus those that didn't to see if there's also anomalies that might be related to an adverse event severity occurrence. Another feature that I'll also be showing with JMP Cclinical and the demonstration around adverse event analysis is the JMP Clinical API that we've built into the system. One of the most difficult things of providing and creating and developing a vertical application that has out-of-the box one-click reports is that you get 90% of the way there and then the customer might say, oh, well, I really wanted to tweak it, or I really wanted to look at it this way, or I need to change the way the data view shows up. So one of the things we've been working hard on in our development team is using JMP scripting JSL to surface an API into the clinical review, to have control over the objects and the displays and the dashboards and the analyses and even the data sets that go into our clinical reviews. So I'll also be showing some of that in the adverse event analysis. So let's back up a little bit and go into the meat of adverse events and clinical trials now that we have an overview of JMP Clinical. There's really two kind of key ways of thinking of this. There's that safety review aspect of a clinical trial where that's typically counts and percentages of the adverse events that might occur. And a lot of the medical doctors, monitors, or reviewers often use this data to understand medical anomalies, you know, a certain adverse event starts showing up more commonly, with one of the treatments that could have medical implications. There's also the statistical signal detection, the idea of statistically assessing our adverse events occurring at an unusual rate in one of the treatment groups versus the other. So here, for example, is a traditional static table that you see in many of the types of research or submissions or communications around a clinical trial adverse event analysis. Basically it's a static table with counts percents and if it is more statistically oriented, you'll see things like confidence intervals and p values as well around things like odds ratios or a relative risks or rate differences. Another way of viewing this can also be visually instead of with a tabular format so signal detection, looking at say odds ratio or the, the risk difference might use the Graph Builder in this case to show the results of a statistical analysis of the incidence of certain adverse events and how they differ between treatment groups, for example. So those are two examples. And in fact, from the work we've done and the customers we've worked with around how they view and have to analyze adverse events, the JMP Clinical system now offers several common adverse event analyses from simple counts and percentages to incidence rates or occurrences into statistical metrics such as risk difference, relative risk, odds ratio, including some exposure adjusted time to event analyses. We can also get a lot more complex with the types of models we fit and really go into mixed or Bayesian models as well in finding certain signals with our adverse event differences. And also we use this data heavily in reviewing just the medical data in either a medical writing narrative or patient profile. So now I'm going to jump right into JMP Clinical with a review that I've built around many of these common analyses. So one of the things you'll notice about JMP Clinical is it doesn't exactly look like JMP, but it is. It's a combined integrated solution that has a lot of custom JSL scripting to build our own types of interfaces. So our starter window here lays out studies, reviews, and settings, for example. And I already have a review built here that is using our example nicardapine data. This is data that's shipped with the product. It's also available in the JMP sample library. It's a real clinical trial, looking at subarachnoid hemorrhage. It was with about 900 patients. And so what this first tab of our review is looking at is just the distribution of demographic features of those patients, how many were males versus females, their race breakdowns, what treatment group they were given, their sites that the data was taken from, etc. So this is very common, just as the first step of understanding your clinical data for a clinical trial. You'll notice here we have a report navigator that shows the rest of the types of analyses that are available to us in this built review. I'm going to walk through each of these tabs, just quickly to show you all the different flavors of ways we can look at adverse events with the clinical trial data set. Now, the typical way data is collected with clinical trials is an international standard called CDISC format, which typically means that we have a very stacked data set format. Here we can see it, where we have multiple records for each subject indicating the different adverse events that might have occurred over time. This data is going to be paired with the demography data, which is one row per each subject as seen here in this demographic. So we have about 900 patients and you'll see in this first report, we have about 5,000 or 5,500 records of different adverse events that occurred. So this is probably the most commonly used reports by many of the medical monitors and medical reviewers that are assessing adverse event signals. What we have here is basically a dashboard that combines a Graph Builder counts plot with an accompanying table, as they are used to seeing these kind of tables. Now the real value of JMP is its interactivity and that dynamic link directly to your data so that you can select anywhere in the data and see it in both places. Or more powerfully, you can control your views with column switchers. Now here we can actually switch from looking at distribution of treatments to sex versus race. You'll notice with race, if we remember, we had quite a few that were white in this study, so this isn't a great plot when we look at it by percent or by counts, so we might normalize and show percents instead. And we can also just decide to look at the overall holistic counts of adverse events as well. Another part of using this as this column switcher is the ability to you know categorize what kind of events those were. Was it a serious adverse event? What was the severity of it? Was the outcome that they are when they recovered from it or not? What was causing it? Was it related to study drug? All of these are questions that medical reviews will often ask to find interesting or anomalous signals with adverse events in their occurrences. Now one of the things you might have already noticed in this dashboard is that I have a control group as column switcher here that's actually controlling both my graph and my table. So when I switched to severity, this table switches as well. This was done with a lot of custom JSL scripting specifically to our purposes, but I'll tell you a secret, in 16 the developer for column switcher is going to allow us to have this type of flexibility so you can tie multiple platform objects into the same columns switcher to drive a complex analysis. I'm going to come back to this occurrence plot, even though it looks simple. Here's another instance of it that's actually looking at overall occurrence where certain adverse events might have occurred multiple times to the same subject. I'm going to come back to these but kind of quickly go through the rest of the analyses and these reviews before coming back to some of the complexities of the simple graph builder and tabulate distribution reports. The next section in our review here is an adverse event incident screen. So here we're making that progression from just looking at counts and frequencies or possibly incidence rates into more statistical framework of testing for the difference in incidence of certain adverse events in one treatment group for another. And here we are representing that with a volcano plot. So we can see actually that phlebitis, hypotension and isothenuria occur much more often in our treatment group, those that were treated with nicardipine, versus those on placebo. So we can actually select those and drill into a very common view for adverse events, which is our relative risk for a cell plot as well, which is lots of lot of times still easier to read when you're only looking at those interesting signals that have possibly clinical or statistical significant differences. Sometimes clinical trials take a long time. Sometimes they're on them for a few weeks, like this study was only a few weeks, but sometimes they're on them for years. So sometimes it's interesting to think of adverse event incidents differences as the trial progresses. We have this capability as well within the incidence screen report where you can actually chunk up the study day, study days into sections to see how the incidents of adverse events change over time. And a good way to demonstrate that might be with an exploding volcano plot here that shows how those signals change across the progression of the study. So another powerful idea with this, especially as you have longer clinical trials or more complex clinical trials, is instead of looking at just direct incidence among subjects you can consider their time to event or their exposure adjusted rate at which those adverse events are occurring. And that's what we offer within our time to event analyses, which once again, shown in a volcano plot looking here using a Kaplan Meier test at differences in the time to event of certain events that occur on a clinical trial. One of the nice things here is that you can select these events and drill down into the JMP survival platform to get the full details for each of the adverse events that had perhaps different time to event outcomes between the treatment groups. Another flavor of time to event is often called an incidence density ratio, which is the idea of exposure adjusted incidence density. Basically the difference here is instead of using some of the more traditional proportional hazards or Kaplan Meier analyses, this is more like a a poisson style distribution that's adjusted for how long they've actually been exposed to a drug. And once again here we can look at those top signals and drill down to the analogous report within JMP using a generalized linear model for that specific type of model with an adverse event signal detection. And we actually even offer some really complex Bayesian analyses. So one of the things with with this type of data is typically adverse events exist within certain body systems or classes...organ classes. And so there is a lot of posts...or prior knowledge that we can impose into these models. And so some of our customers, their biometrics teams decide to use pretty sophisticated models when looking at their adverse events. So, so far we've walked from what I would say consider pretty simplistic distribution views of the data into distributions and just count plots of adverse events into very complex statistical analyses. I'm going to come back now, back to what is that considered simple count and frequency information and I want to spend some time here showing the power of JMP interactivity that we have. As you recall one of the differences here is that this table is a stacked table that has all of the occurrences of our adverse events for each subject, and our demography table, which we know we have 900 subjects, is separate. So what we wanted was not a static graph, like we have here, or what we would have in a typical report in a PDF form, but we wanted to be able to interactively explore our data and look at subgroups of our data and see how those percentages would change. Now, the difficulty is that the percent calculation needs to come from the subject count in a different table. So we've actually done this by formula...like creating column formulas to dynamically control recalculation of percents upon selection, either within categorizing events or, more powerfully, using our review subject filter tool. So here for example, we're looking at all subjects by treatment. Perhaps serious versus not serious adverse events, but we can use this global data filter which affects each of the subject level reports in our review and instantaneously change our demography groups and change our percentages to be interactive to this type of subgroup exploration. So here, now we can actually subgroup down to white females and see what their adverse event percentage and talents are, or perhaps you want to go more granular and understand for each site, how their data is changing for different sites. So what we really have here is instead of a submission package or a clinical analysis where the biometrics team hands 70 different plots and tables to the medical reviewer to go through, sift through, they have the power to create hundreds of different tables and different subsets and different graphics, all in one interface. In fact, you can really filter down into those interesting categories. So if they were looking say at serious adverse events and they wanted to know serious adverse events that were related to drug treatment very quickly, now we got down to a very small subset from our 900 patients to about nine patients that experienced serious adverse events that were considered related to the treatment. So as a medical reviewer this is a place where Ithen might want to understand all of the clinical details about these patients. And very quickly, I can use one of our action buttons from the report to drill down to what's called a kind of a complete patient profile. So here we see all of the information now, instead of at a summary level, at a subject individual level of everything that occurred to this patient over time, including when they had serious adverse events occur and their laboratory or vital measurements that were taken alongside of that. One of the other main uses of our JMP Clinical system along with this medical review, medical monitor is medical writing teams. So another way of looking at this instead of visually in a graphic or even in a table which these are patient profile tables, you can actually go up here and generate an automated narrative. So here we're going to actually launch to our adverse event narrative generation. Again, one of the benefits and values of our JMP Clinical being a vertical application relying on standard data is that we get to know all the data and the way it is formatted up up up front, just by being pointed to the study. So what we can do here is actually run this narrative that is going to write us the actual story of each of those adverse events that occurred. And this is going to open up a Word doc that has all of the details for this subject, their demography, their medical history, and then each of the adverse events and the outcomes or other issues around those adverse events. And we can do this for one patient at a time or we can actually even do this for all 900 patients at a time and include more complex details like laboratory measurements, vitals, either a baseline or before. And so, medical reviewers find this incredibly valuable be able to standardly take data sources and not make errors in a data transfer from a numeric table to an actual narrative. So I think just with that you can really see some of the power of these distribution views, these count plots that allow you to drill into very granular levels of the data. This ability to use subject filters to look either within the entire population of your patients on a clinical trial or within relevant subgroups that you may have found. Now one thing about the way our global filter works through our virtual joins is this is only information that's typically showing the information about the demography. One of the other custom tools that we've scripted into this system is that ability to say, select all subjects with a serious adverse event. And we can either derive a population flag and then use that in further analyses or we can even throw that subject's filter set to our global filter and now we're only looking at serious...at a subject who had a serious adverse event, which was about...almost 300 patients on the clinical trial had a serious adverse event. Now, even this report, you'll see is actually filtered. So the second report is a different type of aspect of a distribution of adverse events that was new in our latest version which is incidence rates. And here, the idea is instead of normalizing or dividing to calculate a percent by the number of subjects who had an event. If you are going with ongoing trials or long trials or study trials across different countries that have different timing startup times, you might want to actually look at the rate at which adverse events occur. And so that's what this is calculating. So in this case, we're actually subset down to any subjects that had a serious adverse event. And we can see the rate of occurrence in patient years. So for example, this very first one, see, has about a rate of 86 occurrences in every 10 patient years on placebo versus 71 occurrences In nicardipine. So this was actually one which this was to treat subarachnoid hemorrhage, intracranial pressure increasing likely would happen if you're not being treated with an active drug. These percents are also completely dynamic, these these incidence rates. So once again, these are all being done by JMP formulas that feed into the table automatically that respect different populations as they're selected by this global filter. So we can look just within say the USA and see the rates and how they change, including the normalized patient years based on the patients that are from just the USA, for example. So even though these reports look pretty simple, the complexity of JSL coding that goes beyond building this into a dashboard is basically what our team does all day. We try to do this so that you have a dashboard that helps you explore the data as you know, easily without all of these manipulations that could get very complex. Now the last thing I wanted to show is the idea of this custom report or customized report. So this is a great place to show it too, because we're looking here at adverse events incidence rates. And so we're looking by each event. And we have the count, or you can also change that to that incidence rate of how often it occurs by patient year. And then an alternative view might be really wanting to see these occurrences of adverse events across time. And so I want to show that really quick with our clinical API. So the data table here is fully available to you. One of the things I need to do first off is just create a numeric date variable, which we have a little widget for doing that in the data table, and I'm going to turn that into a numeric date. Now you'll notice now this has a new column at the end of the numeric start date time of the adverse event. You'll also notice here is where all that power comes from the formulas. These are all actually formulas that are dynamically regenerated based on populations for creating these views. So now that we have a numeric date with this data, now we might want to augment this analysis to include a new type of plot. And I have a script to do that. One of the things I'm going to do right off the bat is just create a couple extra columns in our data set for month and year. And then this next bit of JSL is our clinical API calls. And I'm not going to go into the details of this except for that it's a way of hooking ourselves into the clinical review and gaining access to the sections. So when I run this code, it's actually going to insert a new section into my clinical review. And here now, I have a new view of looking at the adverse events as they occurred across year by month for all of the subjects in my clinical trial. So one of the powers, again, even with this custom view is that this table by being still virtually joined to our main group can still fully respond to that virtual join global subject filter. And so just with a little bit of custom API JSL code, we can take these very standard out-of-the-box reports and customize them with our own types of analyses as well. So I know that was quite a lot of an overview of both JMP Clinical but, as well as the types of clinical adverse event analyses that the system can do and that are common for those working in the drug industry or pharma industry for clinical trials, but I hope you found this section valuable and interesting even if you don't work in the pharma area. One of the best examples of what JMP Clinical is is just an extreme extension and the power of JSL to create an incredibly custom applications. So maybe you aren't working with adverse events, but you see some things here that can inspire you to create custom dashboards or custom add ins for your own types of analyses within JMP. Thank you.   ... View more

  Climate change, water challenges and economic and agricultural hardships are just a few of the challenges we face feeding a growing world population. In this presentation Kelci Miclaus, Advanced Analytics R&D Manager for the JMP Life Sciences division at SAS, guides us through some of the many features in JMP Genomics that are advantageous to plant breeders. Kelci uses Rich Oats for Africa oat-breeding data to show you how JMP Genomics can help build, train and score models to determine what breeds maximize yield, provide better nutritive content and minimize disease. ... View more

Level: Intermediate   Kelci Miclaus, Senior Manager Advanced Analytics R&D, JMP Life Sciences, SAS Mandy Chambers, JMP Principal Test Engineer, SAS   JMP has many ways to join data tables. Using traditional Join, you can easily join two tables together. JMP Query Builder enhances the ability to join, providing a rich interface allowing additional options, including inner and outer joins; combining more than two tables; and adding new columns, customizations and filtering. In JMP 13, virtual joins for data tables were developed that enable you to use common keys to link multiple tables without using the time/memory necessary to create a joined (denormalized) copy of your data. Virtually joining tables gives a table access to columns from the linked tables for easy data exploration. In JMP 14 and JMP 15, new capabilities were added to allow linked tables to communicate with row state synchronization. Column options allow you to set up a link reference table to listen and/or dispatch row state changes among virtually joined tables. This feature provides an incredibly powerful data exploration interface that avoids unnecessary table manipulations or data duplications. Additionally, there are now selections to use shorter column names, auto-open your tables and a way to go a step further, using a Link ID and Link Reference on the same column to virtually “pass through” tables.   This presentation will highlight the new features in JMP with examples using Human Resources data followed by a practical application of these features as implemented in JMP Clinical. We will create a review of multiple metrics on patients in a clinical trial that are virtually linked to a subject demographic table and show how a data filter on the Link ID table enables global filtering throughout all the linked clinical metric (adverse events, labs, etc.) tables. ... View more

Level: Beginner   Kelci Miclaus, Senior Manager Advanced Analytics R&D, JMP Life Sciences, SAS   Conducting translational and clinical research comes with a big price tag. Given costs and time considerations in studies for understanding health outcomes and disease, researchers commonly collect as much data on as many endpoints as possible. This is further motivated by our limited understanding of the genomic underpinnings of disease and good clinical research practice protocols to assess not only efficacy of new therapeutics, but safety and operational integrity. In the life science data “life cycle,” the data volume poses challenges to analyze and communicate results; traditional practices that produce hundreds of tables are neither efficient nor effective. This presentation will focus on visualization to communicate results in several case study analyses typical of life science research. We highlight both data summary visualization techniques and graphs to communicate the results of complex statistical analyses. Examples include volcano plots and Manhattan plots when performing thousands to millions of statistical models with genomic data, distributional summaries of safety and efficacy in clinical trials, laboratory findings trends (waterfall plots, shift plots, spaghetti plots, lasagna plots, swimmer plots) and clinical operational integrity anomaly detection. All examples will be presented with Graph Builder, showcasing the strength and flexibility of this key JMP platform.     ... View more

Level: Intermediate Job Function: Analyst / Scientist / Engineer Kelci Miclaus, Senior Manager Advanced Analytics R&D, JMP Life Sciences, SAS Valérie Nedbal, JMP Senior Systems Engineer, SAS The latest JMP Clinical version 7.0 was released in fall of 2018. JMP Clinical is a vertical solution leveraging JMP capabilities and an extensive JSL infrastructure to create a customized interface for clinical trial data reviews for medical safety, early efficacy, data integrity and oncology analysis. JMP Clinical 7.0 represents significant enhancements that take advantage now more than ever of key features in JMP 14. In this presentation, we will present an overview of the new features of JMP Clinical, focusing heavily on a new comprehensive review subject filter built around virtually joined JMP tables using row state synchronization (new in JMP 14) and data filters. Our JMP Clinical reviews provide standardized, reproducible analyses and now we also introduce an infrastructure for saving further JMP customizations into our reports to allow you to tie in your own JMP analyses to our applications. We also highlight new features in JMP Graph Builder to provide highly customized visualizations for solid tumor oncology clinical trial analysis, including swimmer plots, waterfall plots and spider plots. More generally, we will discuss how this vertical solution exemplifies building robust applications via JSL and the JMP add-in architecture to enhance the use of JMP.   Posted Materials in ZIP file attachment include a PDF of slides that will be presented and several folders of example materials that will be shown in support of understanding JMP Clinical and the infrastructure in JMP that the solution is built upon.  ... View more

Level: Beginner Kelci Miclaus, JMP Life Sciences R&D Manager, SAS   Solid tumor oncology studies have become a major clinical trial focus area and present unique challenges in showing that a new therapy actually works. Due to complex study designs, many traditional statistical techniques (like traditional survival analyses) are not well suited to detect early efficacy signals. Three common visualization techniques have now become mainstream in evaluating multiple measured lesions in solid tumor studies. In this presentation, we will illustrate how to use JMP Graph Builder to build Swimmer Plots, Waterfall Plots and Spider Plots for evaluating tumor response in an early-stage clinical trial. New enhancements to Graph Builder in JMP 14 that allow increased flexibility to control the variables contributing to multiple elements will be highlighted.       ... View more

Kelci Miclaus, JMP Life Sciences R&D Manager, SAS   JMP Clinical and JMP Genomics are aimed specifically at vertical applications for clinical trials and genomic discovery (respectively) in the life sciences research domain. These products integrate JMP and SAS to perform their analyses. Each product presents a custom user interface tailored to the domain area and is developed using the add-in architecture provided by JMP. In this presentation, we will present a high-level overview of how these products function, with specific focus on how we use JMP functionality (JSL scripting, analytical platforms and graphics, dashboards, and JMP data tables) to create a customized analysis interface. In addition to these two formal products, we will discuss several freely available add-ins that our life sciences group created for other areas of life science research.   The attachment provides the slides for this presentation (a large portion will be an online demonstration). There are several links in the presentation to online resources.  ... View more

Kelci Miclaus Advanced Analytics R&D Manager JMP Life Sciences   Abstract: Of the 7.3 billion people on this earth, 3 billion suffer from malnutrition.  Feeding our growing population is one of the most demanding problems of our generation.  How can we improve our crops to have higher yields, better disease-resistance, with ability to grow in harsher environments, while simultaneously increasing vitamin content?  New prediction tools in JMP and JMP Genomics help breeders leverage plants’ genomic variability to find solutions to these complex multi-trait problems.  In this presentation we show how JMP Genomics, a solution that combines JMP with SAS and extensive custom JSL applications, builds predictive models with genomic markers to optimize trait outcomes.  Scoring code from models are used to make potential plant crosses and evaluate predicted outcomes of multiple traits simultaneously.  Interactive JMP graphics using dominant row selection to select Pareto frontier points allow breeders to select potential plant crosses to make in the field that optimize multiple traits based on genetic inheritance.  Essentially, with analytical prediction tools and interactive exploration, we can execute in-silico multi-year breeding programs in a matter of hours.  Corn breeding data from the Brazilian Agricultural Research Corporation (Embrapa) will be used in demonstrations. ... View more

This script builds a dialog for performing Multidimensional Scaling through SAS integration or alternatively by connecting to the R project. MDS is a dimension reduction method that produces coordinates in Euclidean space (usually 2D, 3D) that best represent the structure of a full distance/dissimilarity matrix. MDS requires that input be a symmetric dissimilarity matrix.  Input to this application can be data that is already in the form of a symmetric dissimilarity matrix or the dissimilarity matrix can be computed based on the input data (where dissimilarity measures are calculated between rows of the input data table. NOTE: This script assumes that the SAS preference to use SAS variable labels for column names during import is set! Otherwise you may receive errors of variable names not found.   Refer to this blog for further information or to the ReadMe_MDSAddin.txt found in the Home Folder of the Add-In once installed.   Dialog Screen Capture:   Results Screen Captures:   Summary of MDS fit at different dimensions.  Points can be selected and then results are displayed by button selection.   Two-dimensional results for example Facial Expressions data showing the best fit in two-dimensions of the relationships between judging facial expressions to different stimuli/scenarios. Further details: If the data are not already a dissimilarity matrix, one can be computed (and optionally produced). Details can be found on these metrics in the SAS or R documentation. PROC DISTANCE is used when integrating with SAS. For R: Gower is produced using the daisy() function in the R cluster package. All others are calculated in the dist() function in the R stats package.   Once the data is in the form of a symmetric dissimilarity matrix, MDS can be computed in R using two different R functions (details can be found in the R Documentation): Classical MDS is computed with the cmdscale() function in the R stats package. Kruskal's Nonmetric MDS is computed using isoMDS() function in the R MASS package. FOr SAS, PROC MDS is used and offers more options and styles of MDS algorithms.   For R: All packages that required for this application are present in a basic R project installation For SAS: the SAS/STAT component is required.   Several options/features are performed using JSL scripting: - Creation of the MDS dialog for entering data and parameters. - For R: Standardization of numeric variables if requested prior to distance matrix computation for R.   This will subtract the mean of the column and divide it by the standard deviation of the column. - Output of the distance matrix to a JMP data table. - Resulting dynamic plots for exploring the results of MDS. ... View more

This script uses JSL Scene3D commands (JMP's interface to OpenGL programming) to create a spinning 3-dimensional DNA double helix object.  Authors:   Laurie Cuffney, NCSU   Russ Wolfinger, SAS Institute Inc   Kelci Miclaus, SAS Institute Inc See this SAS Global Forum paper for more details. ... View more

This script creates a dialog to do simulations of a bootstrap simulation to test bootstrap confidence interval coverages. Inspired by Efron (1987) and others' works.  A good example of a case where a bootstrap confidence interval under-covers the true parameter is bootstrapping for the variance of a standard normal distribution. The dialog created by the script allows a user to simulate data from a variety of different distributions and parameters and test the coverage of bootstrap confidence intervals for a handful of common statistics.  Refer to this blog for more information. ... View more

This script performs a simple univariate, nonparametric bootstrap simulation by using the JMP to R Project integration. A JMP Dialog is built by the script where the variable you wish to perform bootstrapping over can be specified. A statistic to compute for each bootstrap sample is chosen and the data are sent to R using new JSL functionality available sincne JMP 9. The boot package in R is used to call the boot() function and the boot.ci() function to calculate the sample statistic for each bootstrap sample and the basic bootstrap confidence interval. The results are brought back to JMP and displayed using the JMP Distribution platform. ... View more

My colleagues are often surprised to hear I went through five majors in college to find my niche in statistics. I started as a physical therapy major, which quickly turned to nuclear medicine, communications, psychology, and finally late in my junior year I became enamored with the beauty of statistics and its real-world applications. I like to think that each period of study helped me grow in a different way. I guess you could say that through my experiences I acted as my own “control” in order to judge what fit me best. I guess you could say that inner yet-discovered statistician in me was already performing an observational crossover study... see where I’m going with this? When I say it’s time to “crossover” to JMP Clinical 4.1, I don’t just mean that we have an amazing new release out that you should get your hands on. Now, with one of the many new features in the latest JMP Clinical version, we have enabled you to analyze crossover trials with your clinical data! JMP Clinical now automatically detects and performs crossover analysis when a study is added that contains the standard timing variables and treatment period variables necessary for crossover support in the ADSL table per the ADAM Implementation Guide. Hopefully, your crossover experiment is better designed than my observational college "study." Key characteristics of a well-designed crossover study include balanced patient randomization to sequences of treatments and sufficient wash-out time between treatment periods with the purpose of studying individual treatment effects. Given these principles, statistical analysis can become straightforward; often instead visualization and interpretation of the data is a complicating factor. Consider a simple two-period AB/BA crossover design. Using mock data from our example Nicardipine study, assume we have a crossover trial for safety analysis with 24 patients. Patients were randomized to receive either Nicardipine (NIC .15) or placebo in the first week and vice versa in the second week. New views and options in the Findings Time Trend Analysis process support crossover visualization to easily see the trends in vital signs (for example) across treatment periods. With this visualization, it’s easy to see when the crossover occurred and follow the trends of average findings measurements by treatment during each period. The option to pool subjects in trends plots when treatment crossover is detected results in a view that looks much like the standard trend view, except that the subjects that comprise the trend line change depending on the treatment administered during the period the findings test was taken. Further options to overlay visits across treatment periods (not applicable with this example but a common request when data is collected across cycles) allow for more customization to visualize/compare data with crossover. Trellis plots now showcase individual patient trends across treatment periods.   Findings Time Trends are not the only features of JMP Clinical that support crossover. Medical monitor distribution views for safety domain events, interventions and findings assign the appropriate treatment period and value based on the date/time of the recorded event/intervention/finding measurement. An example of how the AE distribution counts graph can be customized to categorize occurrence of adverse events by treatment period is shown below. For the more statistically minded, JMP Clinical Incidence Screens support crossover through stratified analysis by patient to compare and report p-values on incidence rates that honor the paired data framework. Results are similar to the typical Incidence Screen volcano plot, where the p-value for testing significant differences in incidence rates (for adverse events for example) is from the Cochran-Mantel-Haenszel (CMH) statistic to account for subject strata. The Breslow-Day test for homogeneity of the odds ratios is also reported in the table. New features like these are not available in other software tools out there, and I strongly believe that no tool can compare to what JMP Clinical has to offer. Personally, every release is an exciting and rewarding time for me when I can see what our development team was able to create and know the impact it can (and will) make on the industry. It reminds me that I made the right “crossover” to study statistics back in college. By the way, it is pure coincidence that I began this blog talking about college…and that “Oh the Places You’ll Go,” which was my inspiration for previous posts on Findings Analysis and Drug-Induced Liver Injury, just happens to be the most common gift given to graduates. It really is a coincidence, but hey it was a great excuse to cross-reference those blog posts so you can learn more about new JMP Clinical 4.1 features! ... View more

In my previous post, I highlighted several of the new features in our latest JMP Clinical release for clinical findings analysis (with a little help from Dr. Seuss). I didn’t use one of my favorite quotes from Oh the Places You’ll Go; so just to get it out of my system, here you go!                 “And will you succeed?  Yes! You will, indeed! (98 and ¾ percent guaranteed.)” I must say Dr. Seuss lived in an optimistic world. If only we could have such high probability of success in clinical trials! So often, clinical trials end in failure, with safety and efficacy being the top offenders. Worse is when a drug has to be withdrawn from the market due to safety-related issues. The leading cause of withdrawal continues to be drug-induced liver injury (DILI), largely because of the complexity of detection and diagnosis and the rare occurrence (as little as 1/10,000) of such events. While true DILI is often not observed until post-market, you can (and should) assess safety signals for potential hepatoxicity concerns. Hy Zimmerman has become famous for coining a set of rules (Hy’s Law) based on time-relevant liver laboratory measurements tied with the comprehensive patient profile to enable early detection of potentially fatal (10-50% mortality) hepatocellular injury and liver damage (Zimmerman 1978,1999). The Hy’s Law Screening process in JMP Clinical has become a state-of-the-art tool just for this purpose, especially with the many new features in our latest release. The analyses, based heavily on the FDA DILI Guidance and domain experts, highlight the JMP Clinical mantra to flag interesting/potential signals through analytically driven visualization and drill-down into comprehensive patient profiles/narratives and cross-domain safety information. New tools that enhance the algorithms to flag potential Hy’s Law cases include a simplified screening plot of Total Bilirubin and Alanine Aminotransferase (mimicking the FDA eDISH tool), histogram distributions of liver tests and demography, interactive tables that are a standard in medical review, and liver test peaks plotted against Study Day. With new drill-down options to plot exposure-annotated time course profiles and liver laboratory shift plots and tables, it is now easier than ever to apply a standardized workflow to detect potential DILI: Visualize peak liver lab values for all patients, flagged by time-relevant algorithm to detect Hy’s Law relevant cases.  Evaluate differences in incidence of transaminase tests between treatment arms.  Report tables of flagged counts and severity of liver elevations.  Drill down to individual time course and shift plots.  Drill down to comprehensive patient profiles, medical history, narratives and further cross-domain clustering and data trends. See screenshots below of some of these new features in JMP Clinical 4.1.         ... View more

A recent post by Richard Zink highlighted the Shift plots process in JMP Clinical software. Shift plots (and shift tables, a recent enhancement to JMP Clinical 4.1) are a key analysis for data from SDTM findings domains (e.g., laboratory, vital signs, etc.), according the FDA ICH E3 guidance. JMP Clinical offers several additional visualization and analysis options for SDTM Findings domains. I am a new mother to an extremely active 7-month-old who is crawling and climbing her way EVERYWHERE. So forgive me that my inspiration for this blog post is not only the FDA guidance, but also another common document resource: Dr. Seuss. When my daughter started crawling, a new world suddenly opened up for her. And in a few weeks, she gained several new skills, thanks to her newfound mobility. I could see her thinking: “Oh, the places you'll go! There is fun to be done!”* Data analysis is a lot like that. When given the right set of tools, you can learn a lot quickly -- finding results you expect as well as unexpected findings that can drive your analysis. “The more that you learn, the more places you’ll go.” JMP Clinical offers several tools for clinical findings data, all of which were developed with consideration of FDA guidelines. The ICH E3 guidance specifies evaluation of laboratory values be analyses of values over time (time trends and distributions of abnormal counts across visits), individual patient changes (shift tables and plots) and individual clinically significant abnormalities (patient narratives and profiles). Here’s a summary of JMP Clinical tools to get you to where you want to go with findings analysis: Histograms, bar charts, analysis of variance (or contingency analysis for character findings) and tables in the distribution process quickly summarize the frequency of laboratory tests, abnormalities, treatment differences and missing results. Count plots shown in Figure 2 are a new feature in JMP Clinical 4.1. Notice the trend of more subjects on treatment (NIC .15) with abnormally high blood urea nitrogen (BUN) levels across visits. Box plots, time trends and bubble plots produce a variety of views to evaluate findings values over time. You can create box plots summarized across the trial or at timed trial intervals, display mean and individual time trends across visits and time points, and follow the bivariate distribution of two findings tests through the dynamic bubble plot across study day. Calculations can be performed for observed or baseline-relative measurements. Analysis results are at the individual level and offer one-click patient profiles (and one-more-click patient event narratives). With many ways to view the data, it can be daunting to review each of the potentially dozens of laboratory tests taken during a clinical trial. “You can get so confused that you'll start in to race down long wiggled roads at a break-necking pace and grind on for miles across weirdish wild space, headed, I fear, toward a most useless place.” For the statistically minded, Baseline ANOVA fits a repeated measures mixed model to test for treatment differences in baseline-adjusted findings measurements. A space-constrained volcano plot view immediately shows tests with treatment differences that may merit further analysis. Drilling down to shift plots, box plots and trend plots gets you to the subject-level view of values across time for selected tests of interest to help streamline the clinical review. Findings Time to Event provides further targeted analysis to track differences in occurrence of specific finding measurement “events.” Last but certainly not least, Hy’s Law Screening provides a rich set of tools for liver enzyme analysis to detect potential drug-induced liver injury. This process was developed directly based on the FDA Guidance for Industry Drug-Induced Liver Injury and vetted by industry experts. New features in JMP Clinical 4.1 include a summary view inspired by the FDA eDISH tool, plots of liver test peaks across study day, counts and percent tables of elevations and Hy’s Law Case detection, and new time profile and liver shift plot drill downs. There is so much to this process that it might just require another blog post! Stay tuned, and in the meantime… “You have brains in your head. You have feet in your shoes. You can steer yourself any direction you choose.” * Dr. Seuss. (1990). Oh, the Places You'll Go!  New York: Random House Children’s Books. ... View more

In 1962, a psychological experiment was performed on 30 students. Thankfully, no one was delivering or receiving electric shocks (as in the famous Milgram experiment that was carried out in 1961). Instead, each student was asked to look at pictures of a woman making faces and rate the facial expression dissimilarities in terms of emotion and content. Now I am a numbers girl myself (although I admit to a dark past that includes a minor in psychology), and I find it intriguing how psychologists can make inferences and conclusions from qualitative, inexact information. You may think I am the pot calling the kettle black since the field of statistics is often accused of being inexact. But I think a better way to interpret statistics is as a science that embraces randomness and inexactness to elucidate systematic patterns. Psychologists likely understand this better than most and rely heavily of statistical methods. One popular method that is perfect for turning students’ impressions of pictures into quantifiable data is Multidimensional Scaling (MDS), a multivariate technique similar to Principal Components Analysis that falls in the class of dimension reduction methods. MDS takes a set of dissimilarities between several objects and finds the best representation of the objects as coordinates, like in a two-dimensional map for example. A classic example is pairwise distances between cities. When MDS is applied to the distances, the result is coordinates for each city that corresponds very closely with their actual locations on a map. MDS has become a popular method in fields such as psychology, ecology, spatial statistics and genetics, to name a few. Now back to our facial expressions example. We have pairwise dissimilarities between 13 different faces made by the woman: 1. Grief at death of mother 2. Savoring a Coke 3. Very pleasant surprise 4. Maternal love, baby in arms 5. Physical exhaustion 6. Something wrong with plane 7. Anger at seeing dog beaten 8. Pulling hard on seat of chair 9. Unexpectedly meets old boyfriend 10. Revulsion 11. Extreme pain 12. Knows plane will crash 13. Light sleep I want to use MDS to try to understand how these emotions are perceived, but JMP doesn’t have a platform to perform MDS. There is, however, an MDS procedure in SAS as well as MDS packages in R, and JMP integrates with both of these! Luckily for me, some brilliant statistician who happens to share my name has already written a JMP add-in that I can get from the JMP File Exchange to run MDS either via SAS or R. I download the add-in and quickly install it (much like an R package works), and I’m in business! (You can download the add-in, too; it's free, but you'll need a SAS login to get it.) I have R installed on my machine, which means JMP can connect to R, send data and submit R code, and I can get my results back in JMP to use some dynamic interactive tools. All I need to do is open up my data table and go to the Add-Ins menu and pick the Multidimensional Scaling command that was created by the add-in. I launch a dialog (shown below), fill in the dissimilarities I want to run MDS on, set up some options and click Run. JMP connects to R (or SAS), and a few seconds later I get results! The first plot that’s launched tells me how well the data fit into coordinates for a series of dimensions. I decide I want to see the MDS results in two dimensions, so I select the point corresponding to 2 on the graph and click the button below the graph to display the MDS results. The display below now tells me a lot of information about how the students perceived the woman’s emotional faces. In the graph below, points that are closer together mean that those facial expressions portrayed more similar emotions. The expressions are colored based on clustering the coordinates to give an idea of how they may group together into a general class of emotion. Some things are predictable, like the patterns in Pain, Grief, and Revulsion, while for others we might learn something interesting. I myself would NOT have associated meeting an old boyfriend with maternal love and a pleasant surprise, but I guess the lady making the faces has had better experiences! One thing I have definitely learned is that using JMP in conjunction with SAS or R provides limitless possibilities for analysis and data exploration. Thanks to the new add-in architecture in JMP, users can extend their analyses in this way, and I encourage all JMP scripters, SAS programmers and R experts out there to submit your own add-ins to the JMP File Exchange so others can benefit from your research and experience! To learn more about JMP add-ins and SAS or R integration, check out some of the papers and talks for SAS Global Forum, the upcoming user group meetings in Las Vegas. Kelci Miclaus, Clay Barker, Jun Ge. 2011 “JMP® as an Analytic Hub: Using JMP to Build Custom Applications via SAS® and R.” Proceedings of the SAS Global Forum 2011 Conference. Cary, NC: SAS Institute Inc. Available at http://support.sas.com/resources/papers/proceedings11/TOC.html. Eric Hill. 2011. “JMP 9 Add-Ins: Taking Visualization of SAS Data to New Heights.” Proceedings of the SAS Global Forum 2011 Conference. Cary, NC: SAS Institute Inc. Available at http://support.sas.com/resources/papers/proceedings11/TOC.html. ... View more

This week we celebrate the 35th anniversary of SAS user group meetings with SGF 2010 (formerly known as SUGI). SAS has exhibited extraordinary growth and success since that first meeting of five users in 1975. Over this time span, we have also seen major advancements in the field of modern statistics, especially in the use of computer-intensive methods. One of the leading methods, which coincidently originated around the same time as SAS, is the bootstrap (Efron, 1979). The basic bootstrap method, as this Bootstrapping Page from Wikipedia explains, works by resampling the data with replacement to form an empirical sampling distribution of a statistic of interest. Like SAS, the bootstrap has become a remarkable tool for statistical inference; Efron’s works alone on this method have been cited over 30,000 times.   Fast-forwarding a decade, John Sall (one of the co-founders of SAS) and a small group of developers began working on JMP in 1989. Like many SAS products, JMP continues to evolve and grow in popularity worldwide. Just a few years later, Ross Ihaka & Robert Gentleman began the now widely used open-source statistical language R, with syntax based on the previous S and S+ languages.   SAS Global Forum is an opportunity to learn about how our users employ SAS products, as well as for users to learn about the latest and greatest advancements in upcoming SAS software. One such announcement for JMP 9 (releasing in the fall of 2010) that I am proud to be a part of is the new integration capabilities of JMP with R. I’d like to show the basic elements of the integration in the context of a bootstrap confidence interval simulation example.   JMP is a wonderful complement to R. The integration with R is surfaced with several new JSL commands that allow you to connect to an install of R on your desktop, send data to and from R, and submit R routines available through the R packages. JMP dialogs can easily be built for parameter input to R as a front-end, and more significantly, JMP’s interactive and dynamic statistical platforms and graphics make for a perfect back-end to R functions. The dialog below is an application that connects to R to create data for a given distribution and perform simulations to test the coverage of bootstrap confidence intervals for a few common statistics.     You may ask yourself why such a simulation would be interesting or relevant. The bootstrap method (and bootstrap confidence interval) is widely used today to estimate properties of a statistic especially when the distribution of the statistic is not well known. But if that distribution happens to be biased or skewed (such as the distribution of the variance), the bootstrap confidence intervals for the statistic can be inaccurate. This application quickly and easily allows you to evaluate the coverage of bootstrap confidence intervals for the most common distributions and statistics.   The output below shows the results of a simulation (1000 runs) for the 95% confidence intervals of the bootstrapped mean (1000 replications) of a Standard Normal distribution (mean of 0 with variance of 1). The R package “boot” is loaded, and the boot.ci() function computes bootstrap intervals by several different methods. Using the Distribution and Graph Builder platforms in JMP, we see that 95% bootstrap confidence intervals using the Basic, Normal, Percentile, and the BCa method accurately cover the true mean (the Coverage column values equal 1 when the confidence interval contains 0, so the Prob entry for Level 1 corresponds to the empirical coverage). The Graph Builder output shows the confidence interval widths are roughly the same both within and between methods.     Here’s the R integration JSL code used to run the bootstrap for the above scenario:     rconn = R Connect(); rconn << Submit("\[ library(boot) # Load Boot package library(boot) RStatFctn <- function(x,d) {return(mean(x))} b.basic = matrix(data=NA, nrow=1000, ncol=2) b.normal = matrix(data=NA, nrow=1000, ncol=2) b.percent =matrix(data=NA, nrow=1000, ncol=2) b.bca =matrix(data=NA, nrow=1000, ncol=2) for(i in 1:1000){ rnormdat = rnorm(30,0,1) b <- boot(rnormdat, RStatFctn, R = 1000) b.ci=boot.ci(b, conf =095,type=c("basic","norm","perc","bca")) b.basic[i,] = b.ci$basic[,4:5] b.normal[i,] = b.ci$normal[,2:3] b.percent[i,] = b.ci$percent[,4:5] b.bca[i,] = b.ci$bca[,4:5] } ]\")); b_basic= rconn << Get(b.basic); b_normal = rconn << Get(b.normal); b_percent= rconn << Get(b.percent); b_bca = rconn << Get(b.bca); rconn << Disconnect();       Using the R Connect() JSL command and assigning it to the object “rconn”, the code sends messages to the JSL scriptable object “rconn” to submit R code via the Submit() command and to retrieve R matrices containing the bootstrap confidence intervals back via the Get() commands.   Now let’s see what happens when we test the coverage of bootstrap confidence intervals for the variance of a standard normal distribution. In the dialog, if you change the Target Value to 1 and choose the Variance as the bootstrap statistic to compute and click Run Simulation, you get the results below. In this case, all four methods under-cover the true value of the variance, including the bias-corrected and accelerated (BCa) method proposed specifically to combat bias and skewness in the bootstrapped statistic’s distribution (Efron 1987).     Note how easy it is to see the confidence intervals that fail to cover the target value in the Basic bootstrap confidence intervals (by selecting the “0” histogram bar under the Basic Coverage distribution) with new features in JMP 9 that grey out points that were not selected. We can also see how intervals that fail in the Basic method perform in the other methods. Other standard JMP tools such as the Data Filter can help to explore these results in ways that cannot easily and quickly be done in R. Likewise, the comparison of the coverage among methods is easy to see with a custom JSL graphics script to create a Venn diagram that shows the counts of when the intervals contained the true parameter (available on the JMP File Exchange and in JMP Genomics).    This application shows just a taste of what you can do with JMP and R together. With a little JSL and the statistical and graphics platforms of JMP coupled with the breadth and variety of packages and functions in R, one can build complete easy-to-use applications for statistical analysis.   JMP can also integrate with SAS, which adds the ability to work with large-scale data through the file-based system as well as the depth and advanced capabilities of SAS procedures. With these seamless integrations, JMP can become a hub that enables you to connect with both SAS and R, as well as provide unique statistical features such as the JMP Profiler and interactive graphic features such as Graph Builder. The possibilities of what you can do with this are endless!   SAS, JMP, R and the field of statistical computing have come a long way over the last 35 years. I am excited to be a part of this company and this industry as we continue to provide software tools that allow you to accomplish your analyses with flexibility, style, and ease!   When JMP 9 is officially released, I will put the script for the bootstrap simulation and others on the JMP File Exchange.   Now let me leave you with a question: If the bootstrap confidence intervals for the variance of a standard normal distribution are under-covered, how do you think the bootstrap confidence intervals of the mean of a Chi-square distribution will behave? ... View more