Findings Time Trends

In this example, report options were set as shown in the following table:

Option Settings

Option Type	Option	Setting
Display	Laboratory Test Results	Blood Urea Nitrogen (mMol/L)
All other options set to the default.

The Laboratory Test Results (LB) domain was selected, unscheduled visits were excluded, and the baseline value was calculated using the baseline flag. Change from baseline was selected as the findings measurement.

This report example summarizes change from baseline in Blood Urea Nitrogen (BUN). It enables reviewers to evaluate average within-subject changes over time and compare trends across treatment groups for BUN measurements collected througout the study. Although no statistical testing is performed, 95% confidence intervals are displayed around the mean change for each treatment group to evaluate potential treatment differences at each follow-up visit. The number of records contributing to each mean is summarized in a bar chart below the graph.

Figure 1: Mean Change from Baseline for BUN

In this example (Figure 1), the mean change in BUN from baseline increased slightly over time in all treatment groups, with the Nicardipine group showing modestly greater increases compared with the placebo group. The mean change in the Nicardipine group ranged from 0.3 to 4.6 mmol/L over the study follow-up period. Differences between the Nicardipine and placebo groups were small and accompanied by overlapping variability, suggesting no clear or clinically meaningful separation. The gradual increase over time might indicate a mild cumulative effect; however, the magnitude of change was limited and should be interpreted in the context of other renal markers, such as creatinine and eGFR, as well as the potential influence of outliers.

Figure 2: BUN Change from Baseline for All Subjects

The all-subjects plot shown in this report (Figure 2) displays individual subject trajectories of change in BUN from baseline over time. This plot (also known as a spaghetti plot) shows the wide spread of values, indicating heterogeneity across subjects. Several subjects appear to have larger increases in BUN than the majority of the study population, which might contribute to the upward shift observed in the mean change-from-baseline plot. This observation reinforces that the mean differences are small relative to with-group variability and might be driven by a small subset of subjects rather than a consistent shift across the cohort.

The interactive nature of these plots enables users to select a line or point on the graph to identify the subject or group of subjects represented by that graphical element. Users can then create a subject filter based on the selected subjects for further exploration. Subjects with the largest increases might warrant additional evaluation in the context of other renal safety markers and adverse event profiles.

Figure 3:  

In this example, we selected two lines (use Shift+Click to select multiple items) in the all-subjects graphs (bold lines in Figure 3), and then clicked Show Subjects (), which displayedd a window that listed the selected subjects. Further exploration of these subjects’ safety profiles can then be performed using the Review Subject Filter in other JMP Clinical Safety reports, such as Acute Kidney Injury, Patient Profiles and the various Adverse Events reports.

In this example, report options were set as shown in the following table:

Option Settings

Option Type	Option	Setting
Data	Plot findings measurements as	Observed Result
	Removed unscheduled visits	unchecked
Display	Laboratory Test Results	Bilirubin (mMol/L)
All other options set to the default.

This report example summarizes observed Bilirubin values across the studies. It enables evaluation of trends over time and across treatment groups for Bilirubin measurements collected during the study, as well as assessment of individual subject patterns. Although no statistical testing is performed, 95% confidence intervals are displayed around the mean value for each treatment group to facilitate assessment of potential treatment differences at each follow-up visit. The number of records contributing to each mean is summarized in a bar chart below the graph.

Figure 4: Mean Observe Result for Bilirubin

From this plot (Figure 4), we observe similar baseline values at Visit 1, suggesting comparable baseline distributions between the treatment groups. We also note the substantially smaller number of records available at follow-up visits, ranging from 21-100 records per treatment group, compared with more than 160 records per treatment group at baseline. The large confidence intervals observed at Visits 8 and 11 might reflect the influence of individual outliers or the smaller sample sizes. Mean Bilirubin values appear to fluctuate over time rather than remain stable. The maximum Lower Limit of Normal (LLN) and minimum Upper Limit of Normal (ULN) are shown as dashed reference lines. (**Geoff to provide some more details on Max LLN and min ULN here. ) At Visit 8, the mean Bilirubin estimate exceeds the minimum ULN.

The All-Subjects plot (Figure 5) shows considerable inter-subject variability. A limited number of subjects exhibit notable bilirubin elevations that exceed the general population trend and warrant subject-level review. For example, at Visit 11, hovering over the outlier in the graph reveals that a value of 0.05666 was observed for subject 141063. This value appears substantially higher than the previous value observed for that subject at Visit 4. This finding warrants additional subject-level review and assessment of clinical relevance, including evaluation for possible Hy’s Law, which can be further explored with the Hy’s Law Screening report.

Specifies the variable used to define the groups to be compared in the report. This variable is typically a planned or actual treatment variable, such as ARM, ACTARM, TRTxxA, or TRTxxP, but it can also be another variable that defines meaningful comparison groups, such as SEX or RACE.

Select None to display the data without grouping by a treatment or comparison variable.

Use this widget to specify whether to analyze measurements from either the Electrocardiogram (EG), Laboratory (LB), or Vital Signs (VS) findings domains.

LB is selected by default.

Use this widget to select specific Findings tests. If you leave this field blank, all tests are run.

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Click to open the Add window (shown below) that lists available test names (shown below).

Checking this box will show all the graphs for every findings test instead of one graph with a column switcher.

This box is checked by default.

Use this field to specify one or more By variables from ADSL to use in your analysis.

Selecting a By variable will split the bar chart and tabulate by the grouping variables selected in this option as well as by the variable selected for the comparison or treatment variable.

By Variables

By variables are optional (in most reports)variable specifications whose values define groups of observations, such as hour, month, or year. By variables can enable analyses to be performed separately on different groups as defined by that variable. Choosing a By variable such as gender (ADSL variable = SEX), for example, groups the subjects by gender and conducts two separate analyses: one for males and one for females.

Alternatively, specifying a By variable enables you to animate an image so that you can see how response values change according to some grouping, like over time.

To Specify One or More By Variables:

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Click to open the Add window (shown below) that lists each variable contained in domain and SUPPxx (if merged) specific to this report

Note: In Demographics Distribution only, variables are taken from ADSL, in addition to the report-specific DS and SUPPPDS (if available) data sets.

In this example, AGE Group 1, RACE, and SEX were selected and added.

Note: If no By variables are specified, the analysis is conducted across all the subjects

Use this widget to specify whether to plot the results either as observed or how they relate to the baseline measurement.

Available options are listed in the following table:

You might want to include unscheduled visits when you are analyzing findings by visit.

By default, this box is checked. When it is checked, records are excluded from the analysis if the VISIT variable exists and contains the text "unscheduled" for those records. If the box is not checked, all records are included regardless of the VISIT status.

For some reports, you might have to specify Visits as the Time Scale to enable this field.

Check the box to include missing visits/time points in the plots.

Use this widget to use the flagged baseline record, last recorded pre-dose measurement, or the mean of all the measurements taken during the baseline time window as the baseline measurement.

By default, the flagged baseline record is used.

The following test measurements are considered when forming the baseline measurement:

Choose Last recorded pre-dose measurement to use the last recorded predose test measurement (out of the test measurement pool generated from the above list) as the baseline measurement.

Choose Mean of recorded pre-dose measurements to average these predose test measurements to form the baseline measurement.

Note: When the selected option to calculate baseline is to use the baseline flag and there are two records flagged as baseline, JMP Clinical uses the last record. This differs from JMP Clinical version 8.1, where the mean of the flagged baseline values was used.

Use this widget to specify which updated snapshot records to include in your analysis. This option is surfaced only in studies where the number of snapshots is greater than 0.

The default state of this option is to show new, modified, and stable - effectively all - snapshot records. To see specific records, you must remove undesired records from the analysis. For example, to surface only the new records, select both Modified and Stable and click . Click to restore deleted records.

Note: JMP Clinical now relies on the JMP Compare Data Tables platform to detect new records. When duplicate rows appear, the platform evaluates them based on their row order. As a result, records are no longer labeled as Non‑Unique or Previously Non‑Unique, and the corresponding options to include those records have been removed.

This filter lets you restrict your analysis to only those subjects that meet specific criteria at the level of the specified Findings domain (EG, LB, or VS) .

This filters uses JMP WHERE clauses to Select Rows that meet the specified criteria. To use this filter, you must enter the JSL (JMP Scripting Language) expressions that constitute the WHERE clause in the appropriate text box for specific filtering of findings. Note: This is an advanced concept that not only requires some basic knowledge of the JSL WHERE clause syntax (similar to, but different from, SQL), but also an understanding of the exact variables in the tables in the study.

Risk indicators are calculated using this subset only.

To specify a Findings filter:

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Click to open the WHERE Clause Builder to construct the desired filter.

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Click to clear the filter.

Note: To filter subjects with a specific event or finding, one could also use the Subpopulation Builder on any domain of interest. For example, filter to all subjects that exhibit cardiac failure ( :Customized Query 01 Name == "Cardiac failure" ) and run all reports on those.

This option lists all of the tests performed over the course of the study. The list is specific to the selected Findings domain and changes when you change the selected domain.