Adaptive Identification of Populations with Treatment Benefit in Clinical Trials: Machine Learning Challenges and Solutions

TL;DR

This paper explores adaptive methods for identifying patient subpopulations benefiting from treatments in clinical trials, integrating machine learning techniques to improve flexibility and efficiency in trial design.

Contribution

It introduces two new meta-algorithms, AdaGGI and AdaGCPI, for subpopulation identification, addressing challenges unique to this problem and extending classical trial adaptivity.

Findings

AdaGGI and AdaGCPI perform well across various simulation scenarios.

The algorithms effectively identify beneficial subpopulations with limited trial budgets.

Insights into the advantages and limitations of each method in different settings.

Abstract

We study the problem of adaptively identifying patient subpopulations that benefit from a given treatment during a confirmatory clinical trial. This type of adaptive clinical trial has been thoroughly studied in biostatistics, but has been allowed only limited adaptivity so far. Here, we aim to relax classical restrictions on such designs and investigate how to incorporate ideas from the recent machine learning literature on adaptive and online experimentation to make trials more flexible and efficient. We find that the unique characteristics of the subpopulation selection problem -- most importantly that (i) one is usually interested in finding subpopulations with any treatment benefit (and not necessarily the single subgroup with largest effect) given a limited budget and that (ii) effectiveness only has to be demonstrated across the subpopulation on average -- give rise to interesting challenges and new desiderata when designing algorithmic solutions. Building on these findings, we propose AdaGGI and AdaGCPI, two meta-algorithms for subpopulation construction. We empirically investigate their performance across a range of simulation scenarios and derive insights into their (dis)advantages across different settings.