Individualized treatment decisions can improve health outcomes, but using data to make these decisions in a reliable, precise, and generalizable way is challenging with a single dataset. Leveraging multiple randomized controlled trials allows for the combination of datasets with unconfounded treatment assignment to better estimate heterogeneous treatment effects. This paper discusses several non-parametric approaches for estimating heterogeneous treatment effects using data from multiple trials. We extend single-study methods to a scenario with multiple trials and explore their performance through a simulation study, with data generation scenarios that have differing levels of cross-trial heterogeneity. The simulations demonstrate that methods that directly allow for heterogeneity of the treatment effect across trials perform better than methods that do not, and that the choice of single-study method matters based on the functional form of the treatment effect. Finally, we discuss which methods perform well in each setting and then apply them to four randomized controlled trials to examine effect heterogeneity of treatments for major depressive disorder.

翻译：个体化治疗决策可以改善健康结局，但利用单数据集以可靠、精准且可推广的方式进行此类决策颇具挑战。通过整合多项随机对照试验，可将无混杂治疗分配的数据集进行联合，从而更准确地估计异质性治疗效应。本文探讨了利用多试验数据估计异质性治疗效应的若干非参数方法：首先将单研究分析方法扩展至多试验场景，继而通过仿真研究（其数据生成场景具有不同水平的跨试验异质性）评估各方法性能。仿真结果表明，直接允许治疗效应存在跨试验异质性的方法优于不允许的方法，且单研究分析方法的选择需依据治疗效应的函数形式。最后，本文讨论了各场景下表现优异的方法，并将其应用于四项随机对照试验，以检验重度抑郁症治疗效应的异质性。