The CDISC Analysis Results Data (ARD) Model is an emerging standard for encoding statistical analysis summaries in a machine-readable format. Its primary objective is to streamline the processes of automation, ensuring reproducibility, promoting reusability, and enhancing traceability.

The newly released {cards} R package, a collaborative effort in the Pharmaverse including Roche, GSK, and Novartis, offers a variety of functions for ARD generation. These range from basic univariate summaries like means and tabulations to complex multivariable summaries encompassing regression models and statistical tests.

While {cards} calculates statistics and stores them in a structured object, it cannot present those results; this, however, is where the {gtsummary} R package shines. The {gtsummary} package offers a modular framework to construct summary tables. It won the American Statistical Association’s 2021 award for Innovation in Statistical Programming and Analytics, and also won first place in Posit BPC’s Table Contest’s pharmaceutical track. The {gtsummary} package was recently refactored to utilize {cards} as its backend, which will allow users to both extract an ARD object from a {gtsummary} table and use an ARD object to construct a {gtsummary} table. 

Our recent experience utilizing the {cards} and {gtsummary} R packages  to prepare a health authority filing highlighted the benefits of using ARDs, including improved automation, reproducibility, reusability, and traceability. We believe that this approach will become increasingly essential for ensuring the quality and efficiency of clinical trial reporting.
Daniel Sjoberg
Genentech
Harnessing CDISC’s Emerging Analysis Results Datasets Standard with the {cards} and {gtsummary} Packages
The CDISC Analysis Results Data (ARD) Model is an emerging standard for encoding statistical analysis summaries in a machine-readable format. Its primary objective is to streamline the processes of automation, ensuring reproducibility, promoting reusability, and enhancing traceability.  The newly released  cards  R package, a collaborative effort in the Pharmaverse including Roche, GSK, and Novartis, offers a variety of functions for ARD generation. These range from basic univariate summaries like means and tabulations to complex multivariable summaries encompassing regression models and statistical tests.  While  cards  calculates statistics and stores them in a structured object, it cannot present those results; this, however, is where the  gtsummary  R package shines. The  gtsummary  package offers a modular framework to construct summary tables. It won the American Statistical Association’s 2021 award for Innovation in Statistical Programming and Analytics, and also won first place in Posit BPC’s Table Contest’s pharmaceutical track. The  gtsummary  package was recently refactored to utilize  cards  as its backend, which will allow users to both extract an ARD object from a  gtsummary  table and use an ARD object to construct a  gtsummary  table.   Our recent experience utilizing the  cards  and  gtsummary  R packages  to prepare a health authority filing highlighted the benefits of using ARDs, including improved automation, reproducibility, reusability, and traceability. We believe that this approach will become increasingly essential for ensuring the quality and efficiency of clinical trial reporting.