Reports examining patient demographics and study visit attendance are described in the table below:.
Compares distributions of demographic variables across treatment arms via a one-way ANOVA or contingency analysis. Compares the distributions of study visit days for each center compared to all other centers combined, and identifies unusual differences.
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 Screening interventions by performing a Cochran-Mantel-Haenszel exact test on all 2x2 tables constructed from event incidence and treatment arm
Screening adverse events by performing a Cochran-Mantel-Haenszel exact test on all 2x2 tables constructed from incidence and treatment arm Screening adverse events by performing a Cochran-Mantel-Haenszel test on all 2x2 tables constructed from event resolution and treatment arm 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 Grouping clinical mortality results by treatment arm and generating Kaplan-Meier Survival Curves with associated statistics Creating Kaplan-Meier Survival Curves for time to study discontinuation and associated statistics, grouped by treatment arm Screening events from a domain by performing a Cochran-Mantel-Haenszel test on all 2x2 tables constructed from event incidence and treatment armNote: This report should be considered as two: DS Incidence Screen and MH Incidence Screen, depending on which domain is specified in the dialog. Fitting Bayesian hierarchical models for adverse events, while taking into account a grouping variable
Visualizing findings measurements across the timeline of a study 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 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 Creating waterfall plots to show the distribution of changes in test measurements for a given Findings domain across subjects Visualizing peak values for lab measurements pertaining to Hy’s Law for detecting potential liver toxicity for all subjects across treatment arms Screening findings measurements for a specified domain one at a time by performing a repeated-measures analysis of variance Oncology1
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. 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. Send2
This process compares generates a static report (PDF or RTF) summarizing the general information and pathological observations for each animal in the study.
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 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. 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. 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. Click on a button corresponding to a subject utility. Refer to the table below for guidance..
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.