Predicting subgroup treatment effects for a new study: Motivations, results and learnings from running a data challenge in a pharmaceutical corporation

TL;DR

This paper discusses a data challenge at a pharmaceutical company focused on identifying subgroups for clinical trials, highlighting approaches, results, and practical insights gained from the competition.

Contribution

It introduces a real-world data challenge for subgroup identification in clinical trials, providing insights into effective methods and practical learnings for future research.

Findings

Multiple approaches to subgroup identification were explored.

Participants successfully predicted treatment effects on unseen data.

The challenge provided valuable insights for statistical practice in pharma.

Abstract

We present the motivation, experience and learnings from a data challenge conducted at a large pharmaceutical corporation on the topic of subgroup identification. The data challenge aimed at exploring approaches to subgroup identification for future clinical trials. To mimic a realistic setting, participants had access to 4 Phase III clinical trials to derive a subgroup and predict its treatment effect on a future study not accessible to challenge participants. 30 teams registered for the challenge with around 100 participants, primarily from Biostatistics organisation. We outline the motivation for running the challenge, the challenge rules and logistics. Finally, we present the results of the challenge, the participant feedback as well as the learnings, and how these learnings can be translated into statistical practice.