This chapter describes the general analysis performed by the JMP Clinical AE Distribution, Events Distribution, Interventions Distribution, and Findings Distribution reports. These descriptions apply generally for any event or intervention domain and can be found in the following sections:
Note: The Findings Distribution report has slightly different analyses and results and is covered briefly in a separate section.
The AE Distribution report is used as an example to describe the analysis performed. This report has the most sophisticated Report Options of all the distribution reports for customizing the resulting reports. Other Events/Interventions domains follow a similar, although simpler, workflow.
AE Distribution requires (or expects) several demographic- and specific domain-related variables to generate full report results. The system is flexible. If a certain variable does not exist, the analysis is still performed without it whenever a related variable can be substituted. For example, in adverse event, required, or expected, variables include:
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a treatment variable (examples include either TRTxxP or TRTxxA, ARM, or a specified comparison variable1),
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Treatment Date/Time variables (required if filtering interventions/events based on study treatment). These can include TRTSDTM, TRTSDTC, TRTEDTM, TRTEDTC (ADSL) or RFXSTDC, RFSTDTC, RFXENDTC, RFENDTC (DM domain), EXSTDTC, and EXENDTC (EX domain),
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AESTDTC (Required),
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The AE Distribution dialog (shown below) enables you to use the term level and group level that are available in your ae.sas7bdat data so that while AEDECOD and AEBODSYS are specified by default variables in the examples shown here, this specification can be customized based on the term levels available in the given Study.
Three parameters, Include serious adverse events only, Event Type and Offset for End of Dosing, are used to specify a domain filter.
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Event Type options rely on the available Date/Time start and end variables referenced above. Options include:
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You can also use the Include the following adverse events: and Filter to Include Adverse Events options based on user-customized condition and/or on new/modified/stable records (when JMP Clinical snapshot comparison is being used)
Three parameters, Select saved subject Filter, Additional Filter to Include Subjects and Select the population to include in the analysis, are used to specify a domain filter.
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If an ADSL table is defined and contains multiple treatment periods (TRTxxP for example) and treatment period start/stop dates, ADSL is merged in and the corresponding treatment and period is assigned based on comparison with AESDTC.
Note: If you want to output static AE percent tables only, the Create Static Report(
) action button should be used.
) action button should be used.The subject counts for each value of relevant demographic variables (Treatment, SEX, RACE, COUNTRY, SITEID, STUDYID (if not constant) are computed and then the reciprocal of the subject count is recorded in the DM or ADSL data set for merging into AE. This value can be used as a frequency weight for each subject that has a specific event to calculate the percent of subjects for that given demographic group that experience the AE.
Note: If cross-over is detected, these demographic counts represent the total subjects for EACH treatment period.
The (filtered) AE data and the (filtered) DM or ADSL table (with computed demographic frequencies) are merged by USUBJID (and treatment period if cross-over is detected)
This option is turned OFF by default.
Percent calculations are performed only if this option is UNCHECKED so that when a unique event is counted only once per subject, you can interpret the frequency of AE counts as the percentage of subjects experiencing the event. The representative adverse event is chosen based on Seriousness (AESER if exists), Severity (AESEV), and computed study day (AESTDY or based on AESDTC)
Note: JMP Clinical assumes accordance with controlled terminology: "Y" to represent severe events, and "MILD", "MODERATE", "SEVERE" to select the serious, most severe event as representative.
For other events/interventions, the representative unique occurrence is chosen based on the first occurrence. The following help documentation for the Count multiple occurrences of an event per subject check box option describes the result of this option.
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Each subject contributes only one time to the occurrence count of an intervention, regardless of how many times that specific intervention might have been applied to the subject.
Note: Representative intervention occurrence per subject is selected by sorting the data and taking the first intervention occurring to a subject based on Study Day.
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Compute counts based on the overall counts of interventions, including multiple occurrences per subject.
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After all of these steps have been completed, the STUDY_ae_xx.sas7bdat results table is used for the JMP results dashboard report.
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Distributions of the event/intervention, descriptive variables about the event/intervention (for example, for AEs this could include Body System, Seriousness, Severity, Causality, Outcome, Action Taken, and so on), and demographic distributions of subjects experiencing the event/intervention
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Column Switchers: Choose the grouping variable used as the demographic comparison for the counts plot and table and a stacking variable to categorize events/interventions (especially useful with adverse events).
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: Generates Patient Profiles for subjects experiencing selected events.
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: Subsets and opens ADSL (or DM, if ADSL is unavailable) for subjects experiencing selected events. A Table of USUBJIDs is also presented.
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: Clusters subjects experiencing selected events based on available covariates.
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: Creates a data set of USUBJIDs for subjects experiencing selected events, which subsets all subsequently run reports to those selected individuals. The currently available filter data set can be applied by selecting the Subject Filter data set in any report dialog on the Filters tab.
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(AE/Events Distribution only): For subjects experiencing selected events, this action button launches Interventions Distribution to summarize the distribution of concomitant medications (CM).
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: For subjects experiencing selected events, this action button generates a stacked Histogram to show subjects across study sites by Trial Time Windows.
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: For subjects experiencing selected events, this action button launches Findings Time Trends to summarize laboratory results (LB) across time.
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: For subjects experiencing selected events, this action button launches Findings Time Trends to summarize vitals signs (VS) across time.
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: For subjects experiencing selected events, this action button launches Findings Time Trends to summarize ECG measurements (EG) across time.
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(Interventions Distribution only): For subjects taking selected medications, this action button launches AE Distribution to summarize the distribution of adverse events (AE).
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Note: The most significant change made in this example is that the Percent Occurrence Threshold has been adjusted from 0 to 5 (circled above).
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Click .
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The results of this analysis are based on summarizing adverse events by Dictionary-Derived Term (AEDECOD) with group organization as Body System or Organ Class (AEBODSYS). Variations of options for this are dependent on the available terms in the domain data set.
The counts (Y axis) represent the count of subjects that experienced the given event term (X axis); the counts are categorized as stacked bar charts for levels of Severity/Intensity (AESEV). This categorization is a result of choosing that variable from the AE Stacking action button.
When a unique occurrence is counted only for each subject/event, the percent of subjects experiencing an event within any of the available demographic grouping variables is calculated interactively by the report. The plot below was generated by clicking Show Percents (circled above).
The output STUDY_aed_xx.sas7bdat data set used to generate these plots and tables contains a row for each subject experiencing each event (optionally by treatment period in a cross-over scenario).
Rows that do not meet the Percent Occurrence Threshold specified on the dialog (5% in this example) are hidden and excluded from analysis. This filter can be interactively changed using the Data Filter option for Percent Occurrence (circled below).
N = Subject number
Note: Xi =1 if the ith subject Si is in the DMG (demographic group) and has an AE value on at least one occasion. If these conditions are not met, Xi has a value of 0.
N = Subject number
Example 1: Using the action buttons on the Output dashboard, select None for and None for and examine the Counts Table tab.
Example 2: Using the action button on the Output dashboard, select Planned Treatment for Period 1 for and None for and examine the Counts Table tab. Note how the values change.
Example 3: Using the action button on the Output dashboard, select Planned Treatment for Period 1 for and Severity/Intensity for and examine the Counts Table tab. Note how the values change.
Note: In the example, the demographic group column changes the percent calculations (also known as changes the value of the denominator used in the formula), while the stacking/categorization variable just partitions the counts and percentages.
The value of percent calculation comparison can be seen clearly in the two plots shown below. The first plot shows the AE Counts graph grouped by RACE and categorized by Serious Event.
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Click Show Percents (located above the plot and circled below).
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When you use the (located on the right side of the report) to filter the records that are shown, the change does NOT affect the demographic group denominator values that are used in the percent calculations. These denominators, as described previously, are derived in the SAS programming of the analysis based on the analysis population. The counts (the numerator in the percent formula) of subjects experiencing the event (and now meeting the data filter criteria) change values to reflect the use of the data filter.
In the following example, the default (Event Type = All) AE Distribution report was run for the Nicardipine sample data and the results were subsequently filtered for Serious Event = Y, as shown below:
88/455 = 19.3% of subjects on Placebo experienced serious vasoconstriction.
In the following example, Sex = F has been selected in the data filter:
Note: You must click (circled above) to clear out prior filters before making a new selection.
Note: If you want to have such demographic filters reflected in the reference population, a pre-specified filter should be used up front, as described below.
For example, on the AE Distribution dialog, you can:
Example 1: Generate a Select saved subject Filter of only females:
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Run DM Distribution Report.
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Select F category in SEX distribution bar chart (or from the data filter). Note how the females are highlighted in all of the distributions.
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Click the action button (circled above).
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Specify this filter as a Select saved subject Filter in the AE Distribution dialog
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Click .
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When filtering is applied up front, the percent values and their interpretation are based on females only:
In this example, the 297 female subjects on placebo make up the denominator of the percent calculation. The females showing vasoconstriction represent the subject count (or numerator) that had the event (206 female subjects that had vasoconstriction). In this case, where filtering is applied BEFORE the analysis, the percent calculation (206/297 = 69.4%) represents the number of females showing vasoconstriction out of FEMALE subjects only on Placebo.
In the example described above, the 455 subjects (male and female) on placebo make up the denominator of the percent calculation. The females s represent the subject count (or numerator) that had the event subject to any data filter specification (206 female subjects that had vasoconstriction). In this case, where filtering is applied AFTER the analysis, the percent calculation (206/455 = 45.3%) represents the number of females showing vasoconstriction out of ALL subjects (not just females) on Placebo.
The examples described, while highlighting the AE Distribution report heavily, also reflect the analyses performed by the other events/interventions distributions.
For example, if you run the AE Distribution report using the Nicardipine example that is shipped with JMP Clinical and select Serious Event using the AE Stacking action button, you see the following Counts Table.
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Click , as shown below.
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JMP Clinical runs the concomitant medications Interventions Distribution report for the 43 selected subjects.
If you click Show Percentages on the dashboard, you see the Counts Graph and Counts Table shown below:
The percent calculations are performed only when a unique event/intervention is counted for each subject in the analysis. If you check the Count multiple occurrences of an event per subject (or Count multiple occurrences of an intervention per subject when running the Interventions Distribution), all records are counted in the results report.
With this option specified, percent values are not computed and the Percent Occurrence Threshold is ignored since the counts of events no longer represent counts of subjects.
Results for the Nicardipine example study when AE Distribution was run for multiple occurrences of events are shown below.
Note: It might be useful to customize the results report data filter to include the computed Total Count column if you want to filter to more commonly occurring adverse events. The grouping and stacking capabilities and action buttons are still available with this view of the report.
The counts now might represent multiple occurrences of the same event for the same subject. For example, when a unique occurrence only was counted (for all Event Types) in Nicardipine, there was a total of 560 subjects experiencing Vasoconstriction.
The distinction in the interpretation of the counts is reflected in the text field above the plots on the Counts Graph tab.
All distributions (AE Distribution, Events Distribution, Interventions Distribution, and Findings Distribution) also include a set of distributions of relevant results variables in the report.
For the Findings Distribution report, these distributions are the initial view. The findings calculations follow the same workflow as described by the events computation, but are simpler because no percent calculations are performed. Most of the analyses are derived using the JMP reports.
The Findings Distribution report might contain the following results:
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Distributions: Contains distributions of parameters from the specified Findings domain.
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Count Plots: Contains graphs for each test to display measurement counts within categories of the Reference Range Indicator variable. This tab is displayed only if the xxNRIND variable is present in the Findings domain data (typically the LB domain).
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Test Results: Contains One-way Analyses (ANOVA) for each test that has numeric measurement results (xxSTRESN values), Contingency Analyses for each Findings test that has character results (xxSTRESC values but missing xxSTRESN values), or both.
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Missing Test Details: Contains tables displaying subject counts for tests that were either not recorded, or that were recorded but have missing measurement values (of xxSTRESN and/or xxSTRESC). If all subjects had nonmissing recorded test measurements for all tests, this tab is not shown.
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The Distribution tab is shown above. The other three tabs are shown below:
Count Plots: This plot shows counts of records from the Findings domain by Study Visits. The xxNRIND and either the VISIT or VISITNUM variables are required in the domain in order to produce these plots.
Missing Test Details; This tab tracks both missing tests and test records that contain missing values.
This table displays subject counts for which Findings tests were either not reported or not recorded. For example, the number of subjects for whom no record was taken for the ALT lab test in the LB domain is displayed in this table for the ALT test row.
This table displays subject counts for any Findings test was that was recorded, but has a missing measurement value (missing an xxSTRESN value for numeric tests or missing an xxSTRESC for categorical tests). This table differs from the Missing Tests Table in that a record was reported for the test for that subject, but the measurement value was missing.
NONE can also be specified here.