Adaptive Identification of Populations with Treatment Benefit in Clinical Trials: Machine Learning Challenges and Solutions
This work addresses the challenge of making clinical trials more adaptive and efficient for identifying treatment benefits in patient subpopulations, representing an incremental advance by integrating machine learning into existing biostatistical frameworks.
The paper tackles the problem of adaptively identifying patient subpopulations that benefit from a treatment in clinical trials, aiming to relax classical restrictions and incorporate machine learning ideas for more flexible and efficient designs. It proposes AdaGGI and AdaGCPI as meta-algorithms and empirically evaluates their performance across simulation scenarios.
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.