# reportdescriptions

The Demographics Distribution report displays the Demographic characteristics for a clinical study. It accomplishes this using both a graphical histogram display (Demographic Details) and a tabular display (Demographic Tables). Optionally, but not by default, it can display statistical comparisons as well.

The Study Visits report generates graphical and tabulation displays of study visit attendance. Additionally, a distribution of study days for each specified visit is shown.

This report determines whether visit dates are unusual for subjects when ordering dates according to visit number.

This report identifies the weekday of study dates and determines whether these dates fall on major holidays.

This report compares the distribution of study visit days for each center compared to all other centers combined, and identifies unusual differences. For example, a site where all visits occur on the same study day can be flagged for further investigation.

This report attempts to identify subjects, by their birthdays or initials, who register at multiple study sites.

The Enrollment Patterns report plots observed and cumulative enrollment by site. It generates uniformly weighted moving average plots to determine whether enrollment patterns within each site appear unusual.

This program is based on a predictive patient enrollment model built on a poisson-gamma distribution, under the assumption that the recruitment has been started, and is currently at an interim time point (Anisimov, V.V. and V.V. Fedorov. 2007. Statist. Med. 26:4958–4975.). The program first estimates the parameters with maximum likelihood method using enrollment data collected so far. The estimated parameters are then used to predicts future enrollment pattern. If the target time will be missed by a user-defined probability, adaptive adjustment will be launched by predicting the number of new centers necessary for the target enrollment to be reached by the deadline.

This report generates an exposure plot for all subjects in a study for an investigational product by dose and exposure time for the safety population treatment. Summaries of exposure duration for subjects are also produced along with a static report of counts of total duration periods for subjects. The duration report and summary can be produced for specific duration times only through use of a duration times list on the Exposure Duration tab. The exposure plot is generated for all subjects across any time of exposure.

The Interventions Distribution report compares distributions of interventions (either concomitant medication or substance use) and demographic variables across treatment arms.

This report screens all interventions by performing a Cochran-Mantel-Haenszel exact test on all 2 x 2 tables constructed from event (concomitant medications or substance use) incidence and treatment arm. Output is a table of multiplicity-adjusted p-values, an accompanying volcano plot of relative risk, and a SAS data set of indicator variables that can be used as input for other reports.

The Adverse Events Distribution report enables you to compare distributions of adverse events across treatment arms for subgroups defined by demographics such as age, sex and race.

This report is used to generate Adverse Events narratives for clinical study reports.

This report creates tabular and graphical overviews of treatment emergent adverse events in the study by treatment arm.

The Adverse Events Incidence Screen report screens all adverse events by performing a Cochran-Mantel-Haenszel exact test (CMH exact test) on all 2 x 2 tables constructed from event incidence and treatment arm. Output includes one or more volcano plots of risk differences when using default settings. However, plots of relative risks or odds ratios can be generated depending on the selected option for the x-Axis for Volcano Plot. If Study ID varies among the subjects for analysis, the test is stratified by Study ID.

This report screens all adverse events by performing a Cochran-Mantel-Haenszel exact test (CMH exact test) on all 2 x 2 tables constructed from event resolution and treatment arm. Resolution is computed directly from the end time of an adverse event and the end time of the trial, or from a resolution time that you specify.

Adverse Events Severity ANOVA report screens all 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. A separate ANOVA is fit for each distinct adverse event. Volcano plots and other output enable efficient screening of adverse event severities that differ between treatment groups. If a patient has multiple instances of a particular adverse event, then those scores are averaged to form a single score for analysis.

The Adverse Events Time to Event report screens all adverse events by performing log-rank and Wilcoxon tests between treatment groups. The time to first occurrence of the adverse event is used as the response.

Mortality Time-to-Event groups clinical mortality results by treatment arm and generates Kaplan-Meier survival curves with associated statistics.

The Discontinuation Time to Event report plots the number of subjects still enrolled in the study versus days completed.

This report compares distributions of events from a domain and demographic variables across treatment arms. This report should be considered as two different reports: DS Distribution and MH Distribution, depending on which domain is specified in the dialog.

This report screens all events from a domain by performing a Cochran-Mantel-Haenszel exact test on all 2 x 2 tables constructed from event incidence and treatment arm. Output is a table of multiplicity-adjusted p-values, an accompanying volcano plot of relative risk, and a SAS data set of indicator variables that can be used as input for other reports. This report should be considered as two different reports: DS Incidence Screen and MH Incidence Screen, depending on which domain is specified in the dialog.

This report compares distributions of SMQs across treatment arms.

This report screens Standard MedDRA Query terms by performing a Cochran-Mantel-Haenszel exact test on all 2 x 2 tables constructed from event incidence and treatment arm. Output is a table of multiplicity-adjusted p-values, an accompanying volcano plot of relative risk and a SAS data set of indicator variables that can be used as input for other reports.

This report fits the multi-level Bayesian hierarchical models of Berry and Berry (Biometrics 60, 418-426, 2004) and Xia, Ma, and Carlin (J Biopharmaceutical Statistics 21, 1006-1029, 2011). Adverse events are modeled taking into account a grouping variable, such as system organ class.

This process screens Standardized Medical Query (SMQ) terms by performing Cochran-Mantel-Haenszel (CMH) tests on all 2 x 2 tables constructed from event incidence and treatment arm. Output is a two-sided plot with a dot plot showing percent occurrence and forest plot showing risk measurement. Additionally there are 4 tables for narrow queries with dictionary-derived terms , broad queries with dictionary-derived terms, broad and narrow queries, and queries by System Organ Class (SOC)..

This report compares distributions of Findings and demographic variables across treatment arms.

This report enables you to visualize findings measurements across the time line of the study.

This report displays the box plots by treatment group representing the change from baseline in measurements for each test for specified findings domain across various times or points in the study. Times can be specified using a list of bracketed times or, alternatively, a number of times can be set to create times that span across the entire study.

This report displays shift plots to compare test measurements for a specified findings domain at baseline versus on-therapy values and performs a matched pairs analysis on average score during baseline and a summary score during the trial. A separate analysis is done for each findings measurement.

This report creates waterfall plots to show the distribution of changes in test measurements for a given Findings domain across subjects (ordered by their magnitude of change). The Findings measurements are summarized based on a specified summary statistic and a waterfall plot is created for each Findings test.

This report tracks a pair of findings measurements over time with an animated bubble plot. Values are linearly interpolated over time. You can select subjects of interest to display their time profiles. For the LB domain, lab measurements are standardized by a reference midrange derived from the lower limit of normal (LLN) and upper limit of normal (ULN) to facilitate comparisons. The standardization centers the values at the midpoint between the LLN and the ULN and scales by (ULN - LLN)/2.

This analysis visualizes peak values for lab measurements pertaining to Hy’s Law for detecting potential liver toxicity for all subjects across treatment arms. Lab measurements for Bilirubin (BILI), Alanine Aminotransferase (ALT), Aspartate Aminotransferase (AST), and Alkaline Phosphatase (ALP) are divided by the upper limit of normal (ULN) and displayed in a scatterplot matrix annotated with Hy's Law reference lines (2*ULN of BILI, 3*ULN of ALT). This analysis also creates reports of the distributions of relevant liver test variables, tables of missing tests and categorized liver elevation levels, and displays of the peak liver test values by Study day.

This report screens all findings measurements for a specified domain one at a time by performing a repeated-measures analysis of variance. The baseline measurements can be considered as a covariate or as a response. A measurement is determined to be a baseline measurement by the xxBLFL variable where xx is substituted with the 2-letter code for the chosen domain for analysis. If this variable does not exist, baseline is calculated from measurements taken on or before day 1 of the study. A time can be specified to determine baseline measurements. A compound symmetry covariance structure is assumed within each subject. A separate model is fit for each lab measurement. You can also separate the data into time s. A volcano plot of the interaction effect and other output enable efficient screening of lab scores that differ between treatment groups. For the LB domain, lab measurements are standardized by a reference range derived from the lower limit of normal (LLN) and upper limit of normal (ULN) to facilitate comparisons.

This report enables you to define events using one or more findings tests to be analyzed in a Time-to-Event analysis. For events defined with more than one test code, it is assumed that these tests are scheduled on the same date/time. If a subject does not experience an event, they are censored on the date of their last available findings data.

This report identifies sets of records that have identical values on more than one occasion within a subject or between subjects within a study site. This report identifies records based on USUBJID and the following covariates (if available): visit number (VISITNUM), location (xxLOC), method (xxMETHOD), position (xxPOS), specimen (xxSPEC), and planned time point (xxTPT).

This report identifies tests from findings domains that have the same result for the entire study.

This analysis assesses the use of terminal digits (either first or last in numerical findings) by study sites when reporting their clinical findings. It can be used to identify those sites that might exhibit biases in rounding issues or other problems with how they report data as compared with all other sites in the study.

This report identifies outliers and sites with excessive (or too few) outliers.

This report 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.

This report identifies unusual frequencies across the entire study or by study visit. This analysis is typically used on categorical variables, though an analysis on continuous outcomes can identify an over- or under-representation of certain values at a site compared to other sites.

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

This report identifies unusual summary statistics across the entire study or by study visit (if requested). This analysis would typically be used on Findings tests representing continuous variables.

Screening Bias allows the analyst to identify any large within-site changes in values between two visits to identify regression to the mean in assessing study entry criteria. Analysis are performed using paired t-tests. An example for subjective criteria is when investigators score a patient at a screening visit and based on this score the patient meets eligibility to get in the study. However, when the patient comes back at the randomization visit, the "more objective" investigator scores the patient noticeably differently. Or, it can pick up noticeable differences between the start and end of a placebo run-in when things are not expected to change.

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. Spider plots may be annotated with new lesions or selected disposition events. 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. For direct lesion measurement tests such as LDIAM or DIAMETER, lesions are summed and compared to the baseline lesion summation by study visit or study day. If a derived summary test such as the sum of diameters or percent change from baseline is run, then the report will plot the recorded values across time for subjects with recorded measurements.

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, representing RECIST evaluation from the RS domain. The output includes a swimmer plot for subjects who have responded favorably to treatment for solid lesion trials per RECIST criteria using CDISC recommended evaluation responses of Complete Response (CR), and Partial Response (PR). Optionally, subjects can be annotated with responses of Stable Disease (SD) and/or Progressive Disease (PD) as well. 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. Using this summarized response, subject counts and percentages of response rates are displayed in a summary table split by Treatment Variable based on report selection. 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 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. Results are summarized in a set of tables.

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

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

This process visualizes findings measurements for each subject across the time-line of the study.

This report generates a summary of the total subject clinic attendance in counts and percentages over the course of the study. Data from SV domain is used and results are presented in RTF or PDF format.

This report generates SAS reports of adverse event counts and percentages by treatment arms, sex, and race, and are organized by body system. All tables are consistent with the ICH E3 guidelines on the structure and contents of clinical reports.

Running this report generates selected standard safety reports on the population meeting the specified filter criteria, in the format(s) specified (RTF or PDF file).

The DSUR/PSUR Report is used to generate a report for updating regulatory agencies regarding the safety of a clinical trial design and execution for the purpose of securing permission for the continuance of the trial..

This report adds data for various domains to the adsl.sas7bdat 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. The report converts all character variables with values N and Y to numeric variables with values 0 and 1, respectively. The resulting output data set is suitable for pattern discovery and predictive modeling. A transposed version of the data set is also produced. Both versions are useful for clustering.

This report calculates Mahalanobis distance based on available data to identify subject inliers and outliers in multivariate space from the multivariate mean. Refer to the JMP documentation on Mahalanobis Distance Measures for statistical details. It also generates results by site to see which sites are extreme in this multivariate space. Mahalanobis distance is plotted on the log scale to allow for easier examination of small scores. The reference line is derived from a transformation of the mean of the approximate chi-square distribution. This report attempts to use as much data as possible. Along with sex and age, it takes all findings test codes by visit number and time number (if available), as well as frequencies of all event and intervention codes per subject. Of course, doing so can lead to missing data particularly for studies that do not appear to have a fixed number of visits or with lots of dropouts. Because Mahalanobis distance cannot be calculated with lots of missing data present, there is an option to delete variables with at least X% of missing data based on the selected population and filters (default of 5%). Of remaining variables, scores are computed for those subjects with complete data. The general strategy of this report is to use as many variables as possible, while letting a few early dropouts fall out of the analysis.

This report is used to identify similar subjects. It does so by constructing a cross domain data set using as much data as possible (subject to user options). Next, it calculates Euclidean distances to compute a distance matrix and performs hierarchical clustering of subjects, across all of the study centers. Findings values are averaged by USUBJID, test code, visit number, and time point (if available) if there are multiple measurements for a visit or time point. The goal of this exercise is 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.

This report clusters subjects within study site for the purpose of identifying similar subjects. It constructs a cross domain data set using as much data as possible (subject to user options). Next, it calculates Euclidean distances to compute a distance matrix and performs hierarchical clustering of subjects within each study center. Findings values are averaged by USUBJID, test code, visit number, and time point (if available) if there are multiple measurements for a visit or time point. The goal of this exercise is 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.

This report generates a dashboard for risk-based monitoring of clinical trials.

This report aids in the review of study domains for a selected set of subjects meeting specified population, WHERE statement, and Data Filter criteria. It merges the relevant data sets based on their review flag values.

This report enables you to review the notes for a selected study. The output of this report includes a series of histograms providing basic statistics on the study.

This report creates a static report (PDF or RTF format) showing the domains required by JMP Clinical, and lists the available and missing domains along with any affected reports.

This report shows the distribution of the review status of selected subjects of interest in a study.

The Variable Report report examines all of the data sets in the selected study and compares the variables that are needed for each JMP Clinical report with the variables that are available in your study. It then prints out a report (either PDF or RTF) listing each JMP Clinical report, indicating the needed variables for each option in the report, whether the needed variables are available, and if the report can be run for the study.

This report generates either a PDF or RTF document listing all the comments for selected subjects recorded in the CO domain.