Data-driven controlled subgroup selection in clinical trials

TL;DR

This paper introduces two novel methods for data-driven subgroup selection in clinical trials, addressing post-selection inference challenges to improve personalized medicine insights while controlling Type I error.

Contribution

The paper proposes generalised linear and isotonic regression-based methods for subgroup identification, with a focus on controlling Type I error in clinical trial analysis.

Findings

Both methods effectively identify subgroups with high treatment effects.

The methods control Type I error under various simulation scenarios.

Sensitivity to modelling assumptions varies between the two methods.

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.