Data-driven controlled subgroup selection in clinical trials

Manuel M. Müller; Björn Bornkamp; Frank Bretz; Timothy I. Cannings; Wei Liu; Henry W. J. Reeve; Richard J. Samworth; Nikolaos Sfikas; Fang Wan; Konstantinos Sechidis

arXiv · 2512.15676

Data-driven controlled subgroup selection in clinical trials

Manuel M. Müller·Björn Bornkamp·Frank Bretz·Timothy I. Cannings·Wei Liu·Henry W. J. Reeve·Richard J. Samworth·Nikolaos Sfikas·Fang Wan·Konstantinos Sechidis

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Abstract

Subgroup selection in clinical trials is essential for identifying patient groups that react differently to a treatment, thereby enabling personalised medicine. In particular, subgroup selection can identify patient groups that respond particularly well to a treatment or that encounter adverse events more often. However, this is a post-selection inference problem, which may pose challenges for traditional techniques used for subgroup analysis, such as increased Type I error rates and potential biases from data-driven subgroup identification. In this paper, we present two methods for subgroup selection in regression problems: one based on generalised linear modelling and another on isotonic regression. We demonstrate how these methods can be used for data-driven subgroup identification in the analysis of clinical trials, focusing on two distinct tasks: identifying patient groups that are safe from manifesting adverse events and identifying patient groups with high treatment effect, while controlling for Type I error in both cases. A thorough simulation study is conducted to evaluate the strengths and weaknesses of each method, providing detailed insight into the sensitivity of the Type I error rate control to modelling assumptions.

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Manuel M. Müller, Björn Bornkamp, Frank Bretz, Timothy I. Cannings, Wei Liu, Henry W. J. Reeve, Richard J. Samworth, Nikolaos Sfikas, Fang Wan, Konstantinos Sechidis. 2025-12-17. Data-driven controlled subgroup selection in clinical trials. https://arxiv.org/abs/2512.15676

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