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.

提出了一种名为 Syntax 的试验设计，通过在多个亚人群中形成合成控制来估计治疗效果，以识别对治疗有效果的亚人群，从而解决患者对同一治疗方式反应不一致的问题。