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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Demographics Distribution

Compares distributions of demographic variables across treatment arms via a one-way ANOVA or contingency analysis.

Study Visits

Generating a graphical display and tabulation of study visit attendance.

Visit Order

Determines whether visit dates are unusual for subjects when ordering dates according to visit number.

Weekdays and Holidays

Identifying the weekday of study dates and determining whether these dates fall on major holidays.

Perfect Scheduled Attendance

Compares the distributions of study visit days for each center compared to all other centers combined, and identifies unusual differences.

Birthdays and Initials

Identifying subjects that might be identical based on birthdays or initials.

Enrollment Patterns

Generates uniformly weighted moving average plots to determine whether enrollment patterns within each site appear unusual.

Patient Recruitment

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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Exposure Summary

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

Interventions Distribution

Comparing Distributions of interventions and demographic variables across treatment arms

Interventions Incidence Screen

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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Adverse Events Distribution

Comparing Distributions of adverse events and demographic variables across treatment arms

Adverse Events Narrative

Generating adverse event narratives for clinical study reports

Treatment Emergent Adverse Events Summary

Creating a tabular and graphical overview of treatment emergent adverse events for the safety population by actual treatment arm

Adverse Events Incidence Screen

Screening adverse events by performing a Cochran-Mantel-Haenszel exact test on all 2x2 tables constructed from incidence and treatment arm

Adverse Events Resolution Screen

Screening adverse events by performing a Cochran-Mantel-Haenszel test on all 2x2 tables constructed from event resolution and treatment arm

Adverse Events Severity ANOVA

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

Adverse Events Time to Event

Screening adverse events by performing log-rank and Wilcoxon tests between treatment groups

Mortality Time to Event

Grouping clinical mortality results by treatment arm and generating Kaplan-Meier Survival Curves with associated statistics

Discontinuation Time to Event

Creating Kaplan-Meier Survival Curves for time to study discontinuation and associated statistics, grouped by treatment arm

Events Distribution

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.

Events Incidence Screen

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.

Standardized MedDRA Queries Distribution

Compares distributions of SMQs across treatment arms.

Standardized MedDRA Queries Incidence Screen

Screens SMQ terms by performing a Cochran-Mantel-Haenszel test on all 2 x 2 tables constructed from event incidence and treatment arm.

Adverse Events Bayesian Hierarchical Model

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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Findings Distribution

Comparing Distributions of findings and demographic variables across treatment arms

Findings Time Trends

Visualizing findings measurements across the timeline of a study

Findings Box Plots

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

Findings Shift Plots

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

Findings Waterfall Plots

Creating waterfall plots to show the distribution of changes in test measurements for a given Findings domain across subjects

Findings Bubble Plot

Tracking a pair of findings measurements over time with an animated Bubble Plot

Hy’s Law Screening

Visualizing peak values for lab measurements pertaining to Hy’s Law for detecting potential liver toxicity for all subjects across treatment arms

Findings ANOVA

Screening findings measurements for a specified domain one at a time by performing a repeated-measures analysis of variance

Findings Time to Event

Defining events using one or more findings tests to be analyzed in a time-to-event analysis

Duplicate Records

Identifying sets of records that have identical values on more than one occasion within a subject or between subjects within a study site

Constant Findings

Identifying tests from findings domains that have the same result for the entire study

Digit Preference

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.

Outliers

Identifies outliers and identifies sites with excessive outliers.

 

Missing Findings

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.

Frequencies

Identifies unusual frequencies by study visit.

Correlated Findings

Calculates pairwise correlations between tests within each findings domain and identifies unusual results at specific study sites.

Summary Statistics

Identifies unusual summary statistics by study visit.

Screening Bias

Identifies within-site mean differences between two selected visits for continuous endpoints to identify regression to the mean in assessing study entry criteria.

Oncology1

Reports are described in the table below:

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Tumor Response

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.

Disease Response Swimmer Plot

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.

Progression Free Survival

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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Animal Profile

This process compares generates a static report (PDF or RTF) summarizing the general information and pathological observations for each animal in the study.

Findings Incidence Report

This process compares distributions of demographic variables across treatment arms via a one-way ANOVA or contingency analysis.

Trial Design

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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Study Visit Report

Generating a report of total subject clinic attendance over the course of a study

Standard Safety Reports

Generating standard safety tabular reports

Adverse Events Report

Generating reports of adverse event counts and percentages by treatment arms, sex, and race

Comment Report

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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Create Cross Domain Data

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

Multivariate Inliers and Outliers

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.

Cluster Subjects across Study Sites

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.

Cluster Subjects within Study 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.

Risk Based Monitoring

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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Domain Viewer

Aids in the review of study domains.

Notes Distribution

Enables you to review notes for a selected study.

Required Domain Report

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.

Review Status Distribution

Displaying the distribution of the review status of subjects in a study

Variable Report

Launches the JMP Clinical Study Variable Report.

Miscellaneous

Reports are described in the table below:.

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Update Study Risk Data Set

Manually enter site-specific descriptive information as well as risk indicator information

Manage Risk Threshold Data Sets...

Enables you to modify or delete risk threshold data sets.