FDA guidance released in 2023 advocates for covariate adjustment as a non-controversial means to enhance the efficiency of statistical analyses. This includes specific recommendations featuring recently proposed methods from academic groups, providing an opportunity for sponsors to realize the practical benefits of innovative approaches. However, they also present challenges for trial teams regarding practical implementation. Bridging the gap between academia, regulation and industry adoption is essential.

This talk presents a case study from Novartis, where FDA feedback referencing the new guidance prompted a targeted and strategic response. We will discuss how we responded to this feedback, our preparation for future regulatory expectations, and the role of R in enabling change and impact.

The case study will highlight lightweight R package solutions ({beeca}) and the work of the ASA-BIOP Covariate Adjustment Working Group ({RobinCar2}). We will discuss the importance of software that is suited for integration with trial analysis workflows in regulated environments and the benefits of collaborating within the open-source community.
Alex Przybylski
Novartis
Adapting to Regulatory Guidance: Covariate Adjustment and R-enabled Submissions
FDA guidance released in 2023 advocates for covariate adjustment as a non-controversial means to enhance the efficiency of statistical analyses. This includes specific recommendations featuring recently proposed methods from academic groups, providing an opportunity for sponsors to realize the practical benefits of innovative approaches. However, they also present challenges for trial teams regarding practical implementation. Bridging the gap between academia, regulation and industry adoption is essential.  This talk presents a case study from Novartis, where FDA feedback referencing the new guidance prompted a targeted and strategic response. We will discuss how we responded to this feedback, our preparation for future regulatory expectations, and the role of R in enabling change and impact.  The case study will highlight lightweight R package solutions ( beeca ) and the work of the ASA-BIOP Covariate Adjustment Working Group ( RobinCar2 ). We will discuss the importance of software that is suited for integration with trial analysis workflows in regulated environments and the benefits of collaborating within the open-source community.