{gtsummary} has become the most-used R package for clinical tables in the R ecosystem, winning awards from the American Statistical Association and Posit. Building on this foundation, we created {crane}, an open-source extension that facilitates reporting requirements in the pharmaceutical space. Because {crane} and {gtsummary} are built upon Analysis Results Datasets (ARDs), study teams can take advantage of this foundation that streamlines the QC process and allows for simple re-use of calculated results in subsequent reporting.

In this session we show how: (1) a vanilla {gtsummary} script instantly upgrades when {crane} is loaded; (2) ARD-based outputs make QC a straightforward operation; (3) LLMs can summarise the language-agnostic ARD results for medical writers in seconds. You’ll leave with a blueprint of how to adapt {crane} for your reporting needs or  (or roll your own) and shorten table turnaround on day one.
Daniel Sjoberg | Davide Garolini
Genentech | Roche
Beyond {gtsummary}: How the {crane} Package Extends the Framework for Pharmaceutical Reporting
 gtsummary  has become the most-used R package for clinical tables in the R ecosystem, winning awards from the American Statistical Association and Posit. Building on this foundation, we created  crane , an open-source extension that facilitates reporting requirements in the pharmaceutical space. Because  crane  and  gtsummary  are built upon Analysis Results Datasets (ARDs), study teams can take advantage of this foundation that streamlines the QC process and allows for simple re-use of calculated results in subsequent reporting.    In this session we show how (1) a vanilla  gtsummary  script instantly upgrades when  crane  is loaded; (2) ARD-based outputs make QC a straightforward operation; (3) LLMs can summarise the language-agnostic ARD results for medical writers in seconds. You’ll leave with a blueprint of how to adapt  crane  for your reporting needs or  (or roll your own) and shorten table turnaround on day one.