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.

翻译：我们研究了在确证性临床试验中自适应识别从特定治疗中获益的患者亚群的问题。此类自适应临床试验已在生物统计学领域得到深入研究，但迄今为止仅被允许有限的自适应程度。为此，我们旨在放宽对此类设计的传统限制，探索如何融入近期机器学习文献中关于自适应和在线实验的研究成果，以提高试验的灵活性和效率。我们发现，亚群选择问题的独特特征——最重要的是：(i)在有限预算下，研究者通常关注于寻找具有任何治疗获益的亚群（而非必然选择效应最大的单一亚组）；(ii)只需证明该亚群的平均疗效——在设计算法解决方案时引发了有趣的挑战和新要求。基于这些发现，我们提出了AdaGGI和AdaGCPI两种用于亚群构建的元算法。我们通过一系列模拟场景实证研究了它们的性能，并深入分析了它们在不同设置下的优势与劣势。