jmpc v4.0 getting started title - jmp software from sas · pdf filegetting started with jmp...

Getting Started withJMP Clinical

“Creativity involves breaking out of established patterns in order to look at things in a different way.”

Edward de Bono

Version 4.0

JMP, A Business Unit of SASSAS Campus DriveCary, NC 27513

JMP Clinical

Getting Started with JMP Clinical

Copyright © 2012, SAS Institute Inc., Cary, NC, USA

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JMP®, SAS® and all other SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS Institute Inc. in the USA and other countries. ® indicates USA registration.

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Getting Started with JMP Clinical 4.01

Checking the Software Installation1 To verify that the software is installed properly, from the Clinical Starter window, select Demo-

graphics > Distribution, as shown below.

1. Note: This document assumes that JMP Clinical 4.0 has been successfully installed on your desktop ma-chine. If you still need to install JMP Clinical 4.0 on your machine, consult the Installation Instructions forJMP Clinical 4.0 guide (http://www.jmp.com/support/clinical/documentation.shtml).

2 Getting Started with JMP Clinical 4.0

• If a JMP Alert error message is generated stating that the file cannot be found, select General Utilities > Generate Clinical Dialogs from XML from the Clinical Starter window.

2 The Application Process (AP) window appears with the default settings loaded, as shown below.

3 Click Run (circled above).

The process should run in a few seconds and generate graphical outputs, which will be described later.

Study Registration and Variable CheckingTo analyze a new study in JMP Clinical, you must first register it. Data for a study must be in either SDTM format (for example, with SAS data sets dm.sas7bdat, ae.sas7bdat, and so on) or ADaM for-mat (for example, adsl.sas7bdat), and a subset of SDTM variables are required in each data set. JMP Clinical provides a way to check the variables in a registered study to verify it can be analyzed in the software. Below we will be working with the supplied Nicardipine data set for example purposes. It has already been registered during installation, but we will register it again for illustration purposes. The Add Study from Folders utility registers a study saved on your local machine and defines the folders where the SDTM and/or ADaM files are located along with a default output folder. These values can be changed later if desired with the Manage Study AP, found in the Studies menu of JMP Clinical.

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Getting Started with JMP Clinical 4.0 3

1 From the Clinical Starter window, select Studies > Add Study from Local Folders.

2 The Application Process (AP) window appears with the default settings loaded.

• Specify a name for your study

• Specify the name and path to the folder containing either the SDTM or the ADaM data sets. You can either use the Choose button to browse to the folder or enter the name and path in the text field.

Note: JMP Clinical accepts data in either SDTM or ADaM format. You must specify either an SDTM or ADaM folder. If SDTM is specified, all of the SDTM data sets must be in the same folder. Likewise, if ADaM is specified, all of the ADaM data sets must be in the same folder. You can specify both SDTM and ADaM. The preferred specification is SDTM plus the corresponding adsl.sas7bdat ADaM data set.

• You can specify a folder containing optional supplementary data or MedDRA terms. You can either use the Choose button to browse to the folder or enter the name and path in the text field.

• Check the Import SAS Transport Files check box if your data is contained in SAS transport files.

• Specify a folder in which to place the study metadata file(s). To avoid any potential confusion or conflicts as you work through this example, we recommend that the output folder that you select is new and empty.

3 The study to be created is defined as shown in the figure below.


4 Click Run (circled in the figure above) to start the process.

The following JMP Clinical Message window is surfaced when the process is complete.

Once the study has been added, the next step is to check the data sets to determine whether the vari-ables that JMP Clinical requires are available. This is a check for SDTM or ADaM compliance, but only for the subset of variables required by JMP Clinical. This process determines whether the variables required for the software to function are in the appropriate domains, and is designed to be as stream-lined and forgiving as possible. You can perform this process from either the JMP Clinical Message window shown above or from the Clinical Starter window.

5 Click Check Required Variables in the JMP Clinical Message window shown above (or select Studies > Check Required Variables from the Clinical Starter window).

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6 Select the study from the Study pull-down menu.

In this case, we will be using the Nicardipine data, so you can choose either Nicardipine or Nicardipine_1.

7 Click the Open button next to the Required Variables Data Set text box to open the data set that is used to compare your variables to a list of expected variables.

Note: JMP Clinical processes require more than 100 variables. Some of these variables are redundant (i.e the software can use different variables for the same purpose, depending on the study's design).

The Options tab has fields to name the output file and the .jsl file that creates the graphic interface. Default names are created if these are left blank. When defaults are used, the results will be overwritten with each new analysis from the same study unless the output directory is changed

8 Click Run.

A .pdf report with several tables listing results of the check, is generated. The most useful tables are those that listing miss variables and affected processes.


In this example, the SAS Variable, VISITNUM is not available from the sv (study visit) domain. Because this variable is missing, we cannot select the Cycles option from some of the downstream application processes.

Note: JMP Clinical looks for the domain name (for example,, DM, AE, CM) and the SAS Variable name when checking for required variables. The SAS Label is not used.

Basic Safety WorkflowTo get an initial overview of the data, the Basic Safety Workflow is the best starting point. This runs a number of different processes and provides a comprehensive initial assessment of the data. In this example, we will use the Nicardipine data for understanding how to interpret the graphs and the vari-ous tools to drill down into the data. As some of the outputs are structured similarly (for example, the Interventions Incidence Screen and AE Incidence Screen), we will not cover each result in depth. Rather, we will review each type of output.

1 Select Workflows > Basic Safety Workflow from the Clinical Starter.

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In this example, we use the Nicardipine_1 study created above. The Actual treatment arm will be used as the variable for any comparison analyses. The Planned arm or a selected variable can be used instead if desired.

Note: A feature of JMP Clinical is that when a selection is made in one process and the process run, that selection or setting carries through the analysis until it is actively changed.

2 Click the Filters tab.


The Filters tab allows for the selection of different populations of individuals to be analyzed and for specifying filtering and analysis criteria. This tab is present on a number of APs.

3 Click the Options tab.

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The Options tab lists the processes to be run in the workflow and also allows for the selection of Term Levels to be used in the AE Incidence Screen. In this example, we will use the defaults for the Nicar-dipine data set.

4 Click Output.

The Output tab is used to specify the names of the output files, the location where the output is to be placed, and whether to create additional output packages.


5 Click Run.

A number of processes run in sequence. When the run is complete a JMP journal containing the results will be surfaced, as shown below.

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The Workflow Builder dialog, displaying all the processes just run, or the Basic Safety Workflow dialog can be opened by clicking either of the buttons at the top. The buttons to launch the results from each process are listed below. We will examine selected sections, as follows:

DM Distribution Results

1 Click DM Distribution to launch the results

The DMDistribution dashboard is shown below.


The dashboard contains several features that are described below. These features are found in the dash-boards of most analytical processes.

• Tabs Panes. Many APs produce more graphics than can be conveniently displayed. To facili-tate analysis, results are grouped thematically into one or more tabs. The results of the analysis shown here include one tab. From left to right at the top of the dashboard, distributions of Age, Sex, Treatment Arm, and Study Site Identifier are displayed. Note that in this instance, Planned Treatment is used instead of the selected Actual Treatment. The Actual Treatment was not present in the domain used (adsl), so the Planned was substituted. On the next line is a One-way analysis of Age by Treatment Arm, a Mosaic Plot of Sex by Treatment Arm and a Mosaic Plot of Race by Treatment Arm. The contingency tables or values associated with each plot are below the graph. In some cases, the values are not visible. Click on the arrows to view the tables. A Data Filter, which can be used to dynamically subdivide the data for closer anal-yses, is located to the right of the graphics

• The Tabs Selector. An interactive list of all of the tabs is located at the upper left of the dash-board. Each “tab” consists of a drop-down menu that can be used to view the tab in the Tabs Pane or in a new window, to view the data set containing the data used to generate the graph-ics, and to view the SAS log.

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• The Action Buttons are platform result-specific drill down capabilities. When selections of subsets of the data are made, further analyses (for example, subject profiles, subject clustering) can be performed on those selections.

2 Scroll right to the Data Filter. Check the Show and Include check boxes.

Clicking on the different options in the Data Filter will filter the graphics and the values. For example, clicking on F under Sex will present values for only the Females in the data set. You can select addi-tional AND filters by holding the Ctrl key. For example, after selecting F under Sex, the calculated val-ues can be further restricted to WHITE under Race by selecting that value from the list. The continuous variable Age can be adjusted with the slider or by clicking into the numeric field (for exam-ple,, 18) and entering a new value.

3 Additional values can be added to the filter by clicking the AND or OR, and then selecting variables from the resulting list. For example, if you want to be able to filter by site, click the +, then select Study Site Identifier from the list and then click Add as shown below.


4 Click the Clear button at the top of the Data Filter to clear out any filtered values.

5 Click the NIC .15 histogram in the Planned Treatment for Period 01 panel, as shown below.

JMP graphics are dynamically linked: when you select rows by clicking on one figure, the correspond-ing rows are highlighted in all of the other graphics (and data tables).

6 Click the Close All button located to the left of the dashboard below the Action Buttons.

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Exposure Summary Results

7 Click Exposure Summary to view the results.

An exposure summary rtf file is generated, which contains the study summarized values as well as the duration of study treatment and number of subjects, summarized by the default selection Duration Time Window of 7.

8 Examine the Exposure Summary dashboard.

The Exposure Summary tab shows the number of subjects by study days, separated by treatment arms. This enables you to see whether there is a bias in the different groups in the study.

9 Click the Duration Distribution tab. This tab is similar to the Demographics Distribution output results.


10 Under the Data Filter, highlight one of the SITEIDs that has a reasonable number of samples, then from the Action Buttons on the left side of the dashboard, select Cluster Subjects.

This will create a cross domain cluster of subjects, so you can see whether there are any similarities among the selected subjects across domains. Subjects are on the x-axis and the domain identifiers are on the y-axis.

11 Highlight a group of subjects in the CM Indications tab by clicking on a section of the dendrogram on the right

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12 Click along the remaining tabs across the top of the dashboard.

Note that the selected subjects are highlighted in figures on all tabs. When numeric values are used (LB, VS and EG Measurements), additional graphs are shown along with the clustered values among the subjects.

13 Click the Close All button on each open dashboard to close.

Concomitant Medication Results

14 Open and examine the output for CM Distribution.


The graphics and filter function similarly to that of the DM Distributions.

We will review Incidence Screen results below.

Mortality Kaplan-Meier Curves Results

15 Click Mortality Kaplan-Meier Curves to launch the results.

The dashboard has one tab containing two plots.

• The Kaplan-Meier curves and the associated statistics are toward the left-hand side of the dashboard. In this case, it is clear that there is no significant difference in mortality between the two treatments.

• The mosaic plot and associated contingency table are located on the right-hand side of the dashboard. This plot graphically demonstrates that the proportion of subject death during the study does not differ between the treatment arms.

16 Click Close All to close this dashboard.

AE Distribution Results

17 Click the AEDistribution link to view the results.

This AEDistribution dashboard is similar to the one shown in DM Distribution Results with one excep-tion - there is now a Tree Map of the Dictionary-Derived Term, as shown below.

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• The Dictionary-Derived terms are sized according to how many reports were generated in the study. Remember that a subject can experience an adverse event more than once.

• You can see that Vasoconstriction (circled above) has a high number of reports for this adverse event in the study. Positioning your mouse pointer over the Vasoconstriction field will bring up the number of reports, 246.

• These results can be filtered using the Data Filter to the right of the dashboard as demon-strated previously. We will show how the Graph Builder can be used to create other views.

18 Select Clinical > General Utilities > Graph Builder from the menu bar at the top of the dashboard to open the Graph Builder utility.

19 Select Study Site Identifier from the panel on the left and “drag” it to the x-axis as shown below.


20 Go back to the AEDistribution dashboard, and click on the AND at the bottom of the filter to add Dictionary-Derived Term to the filtering terms. (Note: You might need to scroll down through the list of columns.)

21 Highlight Vasoconstriction in the Dictionary-Derived Term filter.

22 Click the Red Triangle next to the Dictionary-Derived Term filter, and from the menu, select Order By Count.

23 Check the Show and Include boxes at the top of the Data Filter.

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24 Compare the histogram above with the non-filtered histogram. Note how they vary across the differ-ent sites.

25 Click Close All to close this dashboard.

AE Incidence Screen

26 From the Workflow Journal, click the AEIncidenceScreen (Preferred Term) link to view the results.


• This Relative Risk volcano plot is a graphical representation of the significant relative risks by preferred term.

• The x-axis is the log2 value of the relative risk. Positive values are associated with Nicardipine treatment and negative values are associated with placebo.

• The y-axis is the -log10(Raw p-value) for each relative risk. The dotted red line represents the False Discovery Rate (FDR) corrected p-value cutoff, which in this case is 3.28. Thus, any value above this line and to the right of 0 has a relative risk that is significantly greater in the Nicardipine group, and points above the line to the left of 0 have a significantly elevated risk in the placebo group.

• Phlebitis, hypotension, and isosthenuria are elevated in the treatment group. We can drill down on this with the Action Buttons to the left of the dashboard.

27 Highlight the Phlebitis, Hypotension, and Isosthenuria bubbles in the plot by drawing a rectangle around them with your mouse.

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28 Click Venn Diagram in the Action Buttons pane.


29 There are 25 individuals that had both hypotension and phlebitis. Highlight that number by click-ing on it, and then click Profile Subjects as shown above.

30 When the Profile is generated, select Normal (circled below) in the Display Mode for Findings panel on the left side of the patient profiler dashboard.

31 Examine the Patient Profile.

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• Each Subject's profile is shown individually. You can remove views from within the profile graphic by expanding the outlines to the left and unchecking the boxes within each domain category. The legends are located to the right of the profile, and comments can be added for each individual.

• Selecting each subject separately will in turn create the profile for each respective subject. Multiple subjects can be shown in a single profile by selecting them from the Subjects box.

32 Select the first subject from the Subjects box, then while holding the Shift key down, select the fifth subject. You should have the top five subjects highlighted.

The Profiler automatically updates to show data for the five selected patients.


In the partial view above, multiple lines for a single Adverse Events value indicate that multiple subjects experienced it. For example, two subjects experienced Anemia (circled) during the study period.

33 Click Create Narratives in the lower left side of the Profile Subjects dashboard.

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A Patient Narrative (partially shown below) is created in MS Word for each patient selected in the Profile Subjects dashboard. This narrative can be edited as desired.


34 Close the Profiler and the Venn Diagram windows and return to the AE Incidence Screen dash-board.

The Dot Plot and Relative Risk Plot action buttons will create more traditional views of values high-lighted in the Volcano Plot.

35 Highlight a number of points in the Volcano Plot as before, and then click Tabulate from the Action Buttons pane to create a table with a 0-1 indicator for each subject.

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36 Drag the Unique Subject Identifier to the table as shown below

The modified table is partially shown below:


37 Click the red triangle at the top (circled next to Tabulate) and select Make into Data Table.

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The values in the resulting table can be further analyzed with numerous JMP Clinical analytic tools (for example,, hierarchical clustering, Venn diagrams).

38 Close all open tables and figures except for the Workflow Journal.

Treatment Emergent AE Summary Results

39 From the Workflow Journal, click Treatment Emergent AE Summary to view the results.

A histogram from the Graph Builder is generated on the results dashboard. These can be explored and filtered with the embedded Data Filter.

A separate rtf file with the Treatment Emergent AE Summary is generated with the data in tabular form.


DS Distribution Results

41 From the Workflow Journal, click DS Distribution to launch the results.

These results are similar to those of the previously viewed distributions.


Findings Baseline ANOVA Results

43 From the Workflow Journal, click FindingsBaselineANOVA link to view the results.


• These results are also presented in a summary volcano plot. In this case, the difference between the Nicardipine and placebo is on the x-axis, and the -log10(p-value) for that differ-ence is on the y-axis. The dotted red line represents the FDR corrected-log10(p-value) for sig-nificance. Clearly WBC and BUN are significantly elevated in the Nicardipine versus the placebo group.

• The dendrogram to the right shows the clustering of the LSmean values for the significant findings between the two treatment arms. Similarly changed values will tend to be in the same cluster.

• Further to the right is a Parallel Plot with the LSmean values of the significant findings as well as these findings in 2-dimensional principal component space.

44 Highlight the WBC and BUN points in the volcano plot. From the Action Buttons on the left, click the Box Plots button

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For each selection, box plots by study day (default = 7 groups) are generated. Each point represents a subject, and the Profile Subjects, Cluster Subjects, or Apply Subject Filter function under the Action Buttons can be applied after selecting points on the plot

45 Close the Box Plots window.

46 From the ANOVA Results window and with WBC and BUN still selected, click Shift Plots (under Action Buttons).


A shift plot is created for each point selected from the ANOVA result's volcano plot. The x-axis is the baseline value/upper limit normal, and the y-axis is the overall trial mean/upper limit normal. Changes in a subject over the course of the trial are seen as points deviating from the solid line, either positively or negatively.

47 Close the Shift Plots window.

48 From the ANOVA Results window and with WBC and BUN still selected, click Construct Oneway

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Plots (under Action Buttons).

Two plots are generated from each point selected in the volcano plot. The top plot in each pair is the one-way analysis with box plots and mean/standard deviation indicators. To compare the mean values, click the Red Triangle next to the Oneway Analysis… and choose the desired Compare Means func-tion from the pull-down menu.


The lower plot in each pair is a density plot. Here we can see that there is a trend for WBC values to be larger in the Nicardipine group versus the placebo group (in other words, the Nicardipine curve is right shifted compared with the placebo curve).


Hy’s Law Screening Results

50 Click the Hys Law Screening link to view the results.

In this portion of the output, each point represents a subject and on the lower row of graphs for exam-ple, the BILI/ULN on the y-axis is plotted versus (from left to right), ALT/ULN, AST/ULN, and ALP/ULN, respectively. Subjects can be highlighted and the drill downs under the Action Buttons pane can be used as shown before.

51 Click on the 3D Scatterplot tab to view a three-dimensional representation of the scatterplot matrix with ALT/ULN, AST/ULN and BILI/ULN as the default axes.

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This completes the review of the workflow results. Before continuing onto the next section, close the Workflow Journal. The results can be brought up again by opening the journal file (.jrn) from the out-put directory for this JMP Clinical study.

Dynamic Visualization of Time CoursesWe have already seen how the bubble plot in JMP allows for the visualization of lab findings changing over time. You can also create outputs from other applications in JMP Clinical to see how values change over pre-defined time windows. In this instance, we will use the AE Incidence Screen application.

1 From the Clinical Starter, select Events > AE Incidence Screen.

2 Click the Test tab.

3 In the Trial Time Windows field on the Test tab, enter the following values: [1,3] [3,6] [6,9] [9,12].

The Test tab should look like the figure below.


4 Click Run.

A bubble plot and data filter are generated.

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5 Hold down the <Ctrl> key and select Hypertension, Hypotension, and Phlebitis in the Diction-ary-Derived Terms data filter.

The filtered bubble plot appears as shown below:


6 Click the Dictionary-Derived Term Exploding Volcano tab.

A plot of the filtered data is shown.

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7 Click the play ( ), or forward ( ) or backward ( ) step controls below the bubble plot to view the change in these values over time.


In this case, we can see that over the time windows, Phlebitis increases in relative risk and -log10(p-value) whereas Hypotension decreases. Hypertension increases only modestly. The size of the points represents the number of reports of each adverse event.

ConclusionThis concludes the getting started session with JMP Clinical 4.0. There are more functions available from the Clinical Starter window, and there are advanced functions such as model selection, partial cor-relations and predictive modeling. The specific JMP Clinical applications all function similarly to the ones that we have reviewed here. Details can be found in the online manual by selecting Documenta-tion and Help > User Guide from the Clinical Starter window or by clicking the User Guide button at the bottom of any process dialog.

jmpc v4.0 getting started title - jmp software from sas · pdf filegetting started with jmp...

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