Findings Shift Plots

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 are set to the default.

This example corresponds to Example 1 in the Findings Time Trends report documentation and the Findings Box Plots report documentation, where baseline verses Mean Post-baseline Blood Urea Nitrogen (BUN) values are graphed.

Figure 1: Baseline vs Mean Post-baseline BUN

Figure 1 compares baseline BUN values with Mean Post-baseline BUN values for each subject. Many observations occur above the line of equality, suggesting that BUN values tended to increase during treatment. The wide spread of post-baseline values indicates substantial variability among subjects. A small number of observations are separated from the main cluster, indicating subjects whose BUN values may warrant additional review.

The distribution of post-baseline BUN values is right-skewed, causing many observations to appear compressed within a small area of the plot. Applying a log transformation can improve visualization by spreading observations more evenly across the plotting space, making treatment-group patterns and potential outliers easier to identify. To apply the log transformation, select the Data option and then choose “Log 2 (log (base 2) transformation) from the Log Transformation of Findings Measurements drop-down menu.

Figure 2 displays the same data after applying the log2 transformation.

Figure 2: Log(2) Baseline vs Mean Log(2) Post-baseline BUN

The 45-degree black reference line drawn on the plot represents the line of no change from baseline. Observations above the line indicate higher post-baseline values, while observations below the line indicate decreases from baseline. The greater concentration of points above the line suggests that BUN values generally increased during treatment. Treatment-specific regression lines provide a visual comparison of trends between the Nicardipine and Placebo groups. Differences in slope or position might indicate treatment-related differences that merit further investigation.

This example demonstrates how the Findings Shift Plot Report can quickly identify overall directional changes, treatment-group differences, and individual subjects with unusually large changes. These patterns might be difficult to recognize in listings or summary tables alone. Reviewers can interactively investigate observations by hovering over points to display subject-level details or by selecting regions of the plot. This allows rapid identification of subjects with unusual changes and facilitates follow-up reviews using other JMP Clinical reports. Evaluation of the shift in other renal markers (for example, serum creatinine or calcium) can be done by changing the selection under Laboratory Test Result.

Subjects identified with notable increases in BUN can be further evaluated using related JMP Clinical reports. For example:

The Findings Shift Plots Report revealed a general increase in BUN values during treatment, identified several subjects with unusually large changes, and enabled rapid assessment of treatment-group trends through interactive visualization.

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

Option Settings

Option Type	Option	Setting
Data	Domain	VS (Vital Signs)
	Summary Statistic for Trial Data	Min
	Calculate baseline using:	Last recorded pre-dose measurement
	Removed unscheduled visits	unchecked
Display	Vital Signs	Diastolic Blood Pressure (mmHg)
All other options set to the default.

This example uses the Findings Shift Plots Report to identify subjects who experienced notable decreases in diastolic blood pressure (DBP) following treatment. Such decreases might indicate a pharmacologic treatment effect and help reviewers identify subjects who crossed clinically meaningful blood pressure thresholds.

In this example, measurements taken at both scheduled and unscheduled visits are included. The subject’s last recorded pre-dose measurement is used as the baseline measurement. For the post-baseline measurement, the minimum recorded value for the subject was chosen. Figure 3 displays the resulting plot.

Figure 3: Basline vs Min Post-baseline Diastolic Blood Pressure

The horizontal axis displays each subject’s last pre-dose DBP measurement, while the vertical axis displays the minimum post-baseline DBP value observed during the study. The diagonal line of equality represents no change from baseline. Observations below this line indicate subjects whose minimum post-baseline DBP was lower than their baseline value. Most observations fall below the line of equality, indicating that DBP values generally decreased after treatment in both treatment groups. Several subjects experienced substantially greater decreases than the overall population and appear separated from the main cluster of points.

This plot quickly highlights subjects who experienced the greatest reductions in DBP, allowing reviewers to identify potentially clinically significant blood pressure decreases without reviewing individual listings for all subjects.

Reviewers can select regions of interest directly within the plot to identify the corresponding subjects. In this example, several subjects with large reductions in DBP were selected for further investigation (Figure 4).

Figure 4: Selecting Subjects within the Basline vs Min DBP Plot

The area highlighted in yellow represents the observations selected. Click to display the selected records in a data table (Figure 5). This table can be filtered to create a focused subject subset for detailed review (Figure 6).

Figure 5: Data Table with Selected Subjects Noted in Rows Section

Figure 6: Subset Table of Selected Subjects' Vital Signs

Examination of the selected subjects allows reviewers to determine whether the observed decreases are isolated findings or part of a broader pattern involving other cardiovascular measurements. Additional review of heart rate, systolic blood pressure, adverse events, and concomitant medications might help determine the clinical relevance of the observed reductions.

This example demonstrates how the Findings Shift Plots report can rapidly identify subjects with potentially clinically meaningful decreases in blood pressure and support targeted follow-up review. By linking directly from population-level visualization to subject-level data, reviewers can efficiently prioritize subjects for investigation and assess findings in the context of other vital signs and safety information. Additional review in the Patient Profiles report can provide further subject-level clinical context.

Note: You might notice a difference in baseline vital signs results between JMP Clinical v8.1 and v20. This can happen when the baseline calculation is based on the mean of recorded pre-dose measurements for findings that have a value of "Y" for VSBLF and  some of those rows are missing a value for VSDY. Version 8.1 computes the mean using all rows that have a value of "Y" for VSBLF, regardless of the value of VSDY. Version 20 ignores rows lacking a value for VSDy and computes baseline means using only rows that both have a value of "Y" for VSBLF, and contain a value for VSDY.

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).

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 the drop-down to specify whether to display the mean (specified by default), median, maximum, minimum, or last values to summarize the results of the trial period.

Options are defined in the following table:

By default, JMP Clinical reports unaltered laboratory measurement values (None). In many cases, simply examining the raw numbers can make interpretation somewhat confusing. Normalization of Lab Measurements to accepted values can often ease these difficulties. JMP Clinical offers several methods for normalizing your data.

Note: This option is not available for domains that lack upper or lower limits of normal variables.

Use the drop-down menu to select a normalization to be applied to the baseline and on-therapy measurements.

Available normalizations are described in the following table:

Log transformations can make certain response distributions closer to Gaussian with constant variance and can enable you to draw more accurate statistical conclusions under standard modeling assumptions. They are especially useful for ratio-type measurements or measurements that are always positive and skewed to the right.

Use the drop-down menu to select the transformation to be applied to the baseline and experimental measurements. None is selected by default.

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.

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.