Available Reports
Reports are grouped into functional categories, as listed below:
Demographics and Visits
Reports examining patient demographics and study visit attendance are described in the table below:.
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Compares distributions of demographic variables across treatment arms via a one-way ANOVA or contingency analysis. |
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Generating a graphical display and tabulation of study visit attendance. |
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Determines whether visit dates are unusual for subjects when ordering dates according to visit number. |
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Identifying the weekday of study dates and determining whether these dates fall on major holidays. |
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Compares the distributions of study visit days for each center compared to all other centers combined, and identifies unusual differences. |
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Identifying subjects that might be identical based on birthdays or initials. |
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Generates uniformly weighted moving average plots to determine whether enrollment patterns within each site appear unusual. |
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Estimates patient recruitment parameters and uses these estimates to predicts future enrollment pattern. If the target time will be missed by a user-defined probability, adaptive adjustment launched by predicting the number of new centers necessary for the target enrollment to be reached by the deadline. |
Interventions
Reports describing interventions are described in the table below:
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Generating an exposure summary and plot for all subjects in a study of an investigational product, by dose and exposure time for the safety population, by treatment |
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Comparing Distributions of interventions and demographic variables across treatment arms |
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Screening interventions by performing a Cochran-Mantel-Haenszel exact test on all 2x2 tables constructed from event incidence and treatment arm |
Events
Reports examining patient study events are described in the table below:
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Comparing Distributions of adverse events and demographic variables across treatment arms |
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Generating adverse event narratives for clinical study reports |
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Creating a tabular and graphical overview of treatment emergent adverse events for the safety population by actual treatment arm |
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Screening adverse events by performing a Cochran-Mantel-Haenszel exact test on all 2x2 tables constructed from incidence and treatment arm |
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Screening adverse events by performing a Cochran-Mantel-Haenszel test on all 2x2 tables constructed from event resolution and treatment arm |
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Screening adverse events by performing a mixed-model analysis of variance, with average ranked severity score as the dependent variable and customizable fixed and random effects |
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Screening adverse events by performing log-rank and Wilcoxon tests between treatment groups |
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Grouping clinical mortality results by treatment arm and generating Kaplan-Meier Survival Curves with associated statistics |
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Creating Kaplan-Meier Survival Curves for time to study discontinuation and associated statistics, grouped by treatment arm |
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Comparing Distributions of events from a domain and demographic variables across treatment arms Note: This report should be considered as two: DS Distribution and MH Distribution, depending on which domain is specified in the dialog. |
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Screening events from a domain by performing a Cochran-Mantel-Haenszel test on all 2x2 tables constructed from event incidence and treatment arm Note: This report should be considered as two: DS Incidence Screen and MH Incidence Screen, depending on which domain is specified in the dialog. |
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Compares distributions of SMQs across treatment arms. |
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Screens SMQ terms by performing a Cochran-Mantel-Haenszel test on all 2 x 2 tables constructed from event incidence and treatment arm. |
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Fitting Bayesian hierarchical models for adverse events, while taking into account a grouping variable |
Findings
Reports examining patient findings are described in the table below:.
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Comparing Distributions of findings and demographic variables across treatment arms |
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Visualizing findings measurements across the timeline of a study |
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Displaying Box Plots by treatment group representing the change from baseline in measurements for each test for a specified findings domain across various time windows or points in a study |
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Displaying Shift Plots to compare test measurements for a specified findings domain at baseline versus on-therapy values, and performing a matched pairs analysis on average score during baseline and a summary score during the trial |
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Creating waterfall plots to show the distribution of changes in test measurements for a given Findings domain across subjects |
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Tracking a pair of findings measurements over time with an animated Bubble Plot |
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Visualizing peak values for lab measurements pertaining to Hy’s Law for detecting potential liver toxicity for all subjects across treatment arms |
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Screening findings measurements for a specified domain one at a time by performing a repeated-measures analysis of variance |
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Defining events using one or more findings tests to be analyzed in a time-to-event analysis |
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Identifying sets of records that have identical values on more than one occasion within a subject or between subjects within a study site |
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Identifying tests from findings domains that have the same result for the entire study |
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Assesses the use of terminal digits by study sites when reporting their clinical findings to identify those sites that might exhibit biases in rounding issues or other problems with how they report data. |
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Identifies outliers and identifies sites with excessive outliers.
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Determines whether individuals are missing data for all test codes across all Findings domains, based on data available at each visit number and time point number. |
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Identifies unusual frequencies by study visit. |
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Calculates pairwise correlations between tests within each findings domain and identifies unusual results at specific study sites. |
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Identifies within-site mean differences between two selected visits for continuous endpoints to identify regression to the mean in assessing study entry criteria. |
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Identifies unusual summary statistics by study visit. |
Oncology1
Reports are described in the table below:
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This process uses the TR, TU (optional), and RS (optional) oncology domains to create spider plots showing tumor results and waterfall plots showing Best or Last recorded responses. Measurements plotted on the spider plot and summarized in the waterfall plot represent change or percent change of tumor burden from baseline in measurable/target lesions. |
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This process creates a disease response swimmer plot and tables around either the best or last recorded response rates and calculated objective response rate for solid tumor oncology clinical trials. The results of these assessments are summarized based on selecting either the Best response per subject, based on preferred order: CR, PR, SD, PD for controlled terms, respectively, or Last recorded response. The objective response rate (the sum and percentage of subjects who had CR + PR assessment result) is also calculated and listed in the summary table. |
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Progression free survival is used to describe how long patients can go without the disease without getting worse. This process uses a modified survival plot and hazard ratio plot to compare the number and percentage of patients who have tumor progression or death, with patients who show no tumor progression or death at the cut-off date. |
SEND2
Reports are described in the table below:
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This process compares generates a static report (PDF or RTF) summarizing the general information and pathological observations for each animal in the study. |
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This process compares distributions of demographic variables across treatment arms via a one-way ANOVA or contingency analysis. |
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This process visualizes findings measurements for each subject across the time-line of the study. |
Reports
Reports are described in the table below:
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Generating a report of total subject clinic attendance over the course of a study |
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Generating standard safety tabular reports |
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Generating reports of adverse event counts and percentages by treatment arms, sex, and race |
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Generating a document of study comments obtained from source data pertaining to each subject. |
Cross Domain
Reports using data from across multiple domains are described in the table below:.
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Adding data for various domains to the ADSL table corresponding to incidence of variables in the AE, CM, LB, and MH domains and a summary statistic for LB, EG, and VS domain values |
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Calculating Mahalanobis distance based on available data to detect subject inliers and outliers in multivariate space, and generating results by site to see which sites are extreme. |
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Constructs a cross domain data set and computes a distance matrix and performs hierarchical clustering of subjects across all of the study centers to identify pairs of subjects with a very small distance. This could be an indication that these subjects are in fact the same individual who has enrolled at multiple sites. |
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Constructs a cross domain data set and computes a distance matrix and performs hierarchical clustering of subjects within each study center to identify pairs of subjects with a very small distance. This could be an indication that these subject are slightly modified copies of one another. |
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Generates a dashboard for risk-based monitoring of clinical trials |
Data Management
Click on a button corresponding to a subject utility. Refer to the table below for guidance..
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Aids in the review of study domains. |
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Enables you to review notes for a selected study. |
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Checks SAS data sets in the SDTM and ADaM folders that have been specified for the selected study, for all variables required for various JMP Clinical reports. |
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Displaying the distribution of the review status of subjects in a study |
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Launches the JMP Clinical Study Variable Report. |
Miscellaneous
Reports are described in the table below:.
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Manually enter site-specific descriptive information as well as risk indicator information |
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Enables you to modify or delete risk threshold data sets. |