pharmaversesdtm

[(https://pharmaverse.org)

Test data (SDTM) for the pharmaverse family of packages

• Purpose
• Installation
• Data Sources
• Naming Conventions
• How To Update
• Documentation Process

Purpose

To provide a one-stop-shop for SDTM test data in the pharmaverse family of packages. This includes datasets that are therapeutic area (TA)-agnostic (DM, VS, EG, etc.) as well TA-specific ones (RS, TR, OE, etc.).

Installation

The package is available from CRAN and can be installed by running install.packages("pharmaversesdtm"). To install the latest development version of the package directly from GitHub use the following code:

if (!requireNamespace("remotes", quietly = TRUE)) {
  install.packages("remotes")
}

remotes::install_github("pharmaverse/pharmaversesdtm", ref = "main") # This command installs the latest development version directly from GitHub.

Data Sources

Some test datasets have been sourced from the CDISC pilot project, while other datasets have been constructed ad-hoc by the {admiral} team. Please check the Reference page for detailed information regarding the source of specific datasets.

Naming Conventions

• Datasets that are TA-agnostic: same as SDTM domain name (e.g., dm, rs).
• Datasets that are TA-specific: domain_TA_others, others go from broader categories to more specific ones (e.g., oe_ophtha, rs_onco, rs_onco_irecist).

Note: If an SDTM domain is used by multiple TAs, {pharmaversesdtm} may provide multiple versions of the corresponding test dataset. For instance, the package contains ex and ex_ophtha as the latter contains ophthalmology-specific variables such as EXLAT and EXLOC, and EXROUTE is exchanged for a plausible ophthalmology value.

How To Update

Firstly, make a GitHub issue in {pharmaversesdtm} with the planned updates and tag @pharmaverse/admiral so that one of the development core team can sanity check the request. Then there are two main ways to extend the test data: either by adding new datasets or extending existing datasets with new records/variables. Whichever method you choose, it is worth noting the following:

• Programs that generate test data are stored in the data-raw/ folder.
• Each of these programs is written as a standalone R script: if any packages need to be loaded for a given program, then call library() at the start of the program (but please do not call library(pharmaversesdtm)).
• Most of the packages that you are likely to need will already be specified in the renv.lock file, so they will already be installed if you have been keeping in sync–you can check this by entering renv::status() in the Console. However, you may also wish to install {metatools}, which is currently not specified in the renv.lock file. If you feel that you need to install any other packages in addition to those just mentioned, then please tag @pharmaverse/admiral to discuss with the development core team.
• When you have created a program in the data-raw/ folder, you need to run it as a standalone R script, in order to generate a test dataset that will become part of the {pharmaversesdtm} package, but you do not need to build the package.
• Following best practice, each dataset is stored as a .rda file whose name is consistent with the name of the dataset, e.g., dataset xx is stored as xx.rda. The easiest way to achieve this is to use usethis::use_data(xx)
• The programs in data-raw/ are stored within the {pharmaversesdtm} GitHub repository, but they are not part of the {pharmaversesdtm} package–the data-raw/ folder is specified in .Rbuildignore.
• When you run a program that is in the data-raw/ folder, you generate a dataset that is written to the data/ folder, which will become part of the {pharmaversesdtm} package.
• The names and sources of test datasets are specified in R/*.R, for the purpose of generating documentation in the man/ folder.

Note: The documentation process in {pharmaversesdtm} is automated for consistency and ease of maintenance. Metadata for each dataset, such as names, labels, descriptions, authors, and sources, is managed in a centralized JSON file (inst/extdata/sdtms-specs.json) and used to generate .R documentation files. See the Documentation Process for details.

Adding New SDTM Datasets

• Create a program in the data-raw/ folder, named <name>.R, where <name> should follow the naming convention, to generate the test data and output <name>.rda to the data/ folder.
  • Use CDISC pilot data such as dm as input in this program in order to create realistic synthetic data that remains consistent with other domains (not mandatory).
  • Note that no personal data should be used as part of this package, even if anonymized.
• Run the program.
• Reflect this update by adding the relevant information to the inst/extdata/sdtms-specs.json file.
• Run data-raw/create_sdtms_data.R in order to update NAMESPACE and update the .Rd files in man/.
• Add your GitHub handle to .github/CODEOWNERS.
• Update NEWS.md.

Updating Existing SDTM Datasets

• Locate the existing program <name>.R in the data-raw/ folder, update it accordingly.
• Reflect this update by adding the relevant information to the inst/extdata/sdtms-specs.json file.
• Run the program, and output updated <name>.rda to the data/ folder.
• Run data-raw/create_sdtms_data.R in order to update NAMESPACE and update the .Rd files in man/.
• Add your GitHub handle to .github/CODEOWNERS.
• Update NEWS.md.

Documentation Process

The documentation process in {pharmaversesdtm} is automated for consistency and ease of maintenance. Metadata for each dataset, such as names, labels, descriptions, authors, and sources, is managed in a centralized JSON file (inst/extdata/sdtms-specs.json) and used to generate .R documentation files.

This streamlined approach aligns with best practices for efficient package development.