Clinical trials are typically run in order to understand the effects of a new treatment on a given population of patients. However, patients in large populations rarely respond the same way to the same treatment. This heterogeneity in patient responses necessitates trials that investigate effects on multiple subpopulations - especially when a treatment has marginal or no benefit for the overall population but might have significant benefit for a particular subpopulation. Motivated by this need, we propose Syntax, an exploratory trial design that identifies subpopulations with positive treatment effect among many subpopulations. Syntax is sample efficient as it (i) recruits and allocates patients adaptively and (ii) estimates treatment effects by forming synthetic controls for each subpopulation that combines control samples from other subpopulations. We validate the performance of Syntax and provide insights into when it might have an advantage over conventional trial designs through experiments.

翻译：临床试验通常旨在理解新疗法对特定患者群体的影响。然而，大规模人群中的患者对相同疗法的反应往往各异。这种患者反应的异质性要求开展能够探究多亚群效应的试验——尤其在某一疗法对整体人群效果微弱甚至无效，却可能对特定亚群具有显著益处的场景下。基于这一需求，我们提出Syntax探索性试验设计，它能从众多亚群中识别出存在正向治疗效果的亚群。Syntax具有样本高效性，这是因为其（i）采用自适应方式招募和分配患者，（ii）通过为每个亚群构建整合其他亚群对照样本的合成对照来估计治疗效果。我们通过实验验证了Syntax的性能，并揭示了其在何种情境下相较于传统试验设计可能具有优势。