Subject Data Review

The subject data review (SDR) is a process that is used to ensure that the data collected from the subjects is in a manner that is consistent with the study protocol and knowledge about the drug. SDR is step 4 in the PumasCP workflow and provides functionality that empowers the analysts to review the data collected from the subjects and identifying any potential issues or concerns. The result artifacts of SDR include Tables, Listings, and Figures (TLFs) that are used to summarize the data collected from the subjects.

The TLFs generated in the SDR step are are all predefined and are generated based on the user's input in the previous steps of the workflow. Each of these tables and listsing can be tweaked for variables or grouping columns, or statistical analysis, and can be saved as a template for future use. In case of figures, there are multiple formatting options that are available to customize the appearance of the figure. Each of the PumasCP modules (NCA, BE, DP, SP) may have different set of default tables and listings, and the user can customize these as per their requirements. Below is a brief description of the TLFs generated in the SDR step of the PumasCP workflow.

Tables

Table One: Demographics

"Table 1" is a common term for the first table in a paper/report that summarizes demographic and other subject level data of the population that is being studied. In general terms, it is a table where different columns from the source table are summarized separately, stacked along the rows. The types of analysis can be chosen manually, or will be selected given the column types. Optionally, there can be grouping applied along the columns as well.

This table is generated based on the mapped variables and grouping columns provided by the user in Step 3 of the PumasCP workflow, Map and Group Subjects. In order to generate this table, PumasCP takes the unique subjects per group and summarizes the unmapped columns in the dataset.

Summary Tables

A summary table summarizes the raw data from one column of a source table for different groups defined by grouping columns. It is similar to a listingtable (described below) without the raw values.

Listings

Listing Tables

A listing table displays the raw data from one column of a source table, with optional summary sections interleaved between. The row and column structure of the listing table is defined by grouping columns from the source table. Each row of data has to have its own cell in the listing table, therefore the grouping applied along rows and columns must be exhaustive, i.e., no two rows may end up in the same group together. The listing tables in PumasCP are designed to show two specific kinds that are used in pharmacokinetic analysis, the concentration-time data and the PK parameters.

Figures

Pre-clinical and Clinical Pharmacology studies require a common set of exploratory data analysis figures that are important to interpret the data and make some initial inferences. Further, these figures also help in identifying any potential issues or concerns with the data collected from the subjects and form the core set of figures that go into the reports, either in the main body or as an appendix. The following figures are generated in the SDR step of the PumasCP workflow:

Observation vs Time by Subject

This figure is a scatter/line plot of the observations vs time for each subject. The x-axis is the time variable and the y-axis is the observation variable. By default, each subject will be in their own panel, and if a subject is separated into multiple groups, each group per subject will be in its own panel. The user can customize the appearance of the figure by changing the color, shape, and size of the points, and the line type and width.

Summary Observation vs Time

This figure is a scatter/line plot of the summary of the observations vs time for each group. The x-axis is the time variable and the y-axis is the observation variable. By default, each group will be in its own panel. The user can customize the appearance of the figure by changing the color, shape, and size of the points, and the line type and width. The user can also choose to display the plot as an overlay of the groups to give a spaghetti plot overlaid by the mean per each group to visualize the spread of the data.

Subject Fit Plots

This plot is specific to the NCA workflow and is a scatter plot of the observations vs the model fit for each subject. The x-axis is the time variable and the y-axis is the observation variable. By default, each subject will be in their own panel, and if a subject is separated into multiple groups, each group per subject will be in its own panel. Each profile will have the log-linear regression line based on the selected points and a title that provides the lambdaz and adjusted R2 value. The user can customize the appearance of the figure by changing the color, shape, and size of the points, and the line type and width.