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 improve the power to 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：个性化治疗决策能够改善健康结局，但利用单一数据集以可靠、精准且可推广的方式制定这些决策颇具挑战。通过整合多项随机对照试验，可将具有无混杂治疗分配的多个数据集相结合，从而提升异质性治疗效果估计的统计效能。本文探讨了利用多项试验数据估计异质性治疗效果的若干非参数方法。我们将单研究方法扩展至多项试验场景，并通过模拟研究（生成具有不同跨试验异质性水平的数据场景）评估其性能。模拟结果表明：允许跨试验治疗效应异质性的方法优于不允许的方法，且单研究方法的选择取决于治疗效应的函数形式。最后，我们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果的异质性。