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.