论文标题
在临床试验中对具有治疗益处的人群的自适应识别：机器学习挑战和解决方案
Adaptive Identification of Populations with Treatment Benefit in Clinical Trials: Machine Learning Challenges and Solutions
论文作者
论文摘要
我们研究了在确认临床试验期间适应从给定治疗中受益的患者亚群的问题。这种适应性临床试验已在生物统计学中进行了彻底研究，但到目前为止仅允许有限的适应性。在这里，我们旨在放松对此类设计的经典限制，并研究如何将有关自适应和在线实验的机器学习文献中的想法融合在一起，以使试验更加灵活和高效。我们发现，亚种群选择问题的独特特征 - 最重要的是，（i）通常有兴趣找到具有任何治疗益处的亚群（鉴于预算有限，并且（ii）在平均范围内，只能证明（ii）有效性在平均范围内证明（ii）在提出有趣的挑战和新的desididerata时，必须证明（ii）有效性。在这些发现的基础上，我们提出了Adaggi和Adagcpi，这是两个用于亚群构造的元算法。我们从经验上研究了它们在一系列仿真方案中的性能，并获得了对它们（DIS）优势在不同环境中的优势的见解。
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.
