Estimating treatment effects conditional on observed covariates can improve the ability to tailor treatments to particular individuals. Doing so effectively requires dealing with potential confounding, and also enough data to adequately estimate effect moderation. A recent influx of work has looked into estimating treatment effect heterogeneity using data from multiple randomized controlled trials and/or observational datasets. With many new methods available for assessing treatment effect heterogeneity using multiple studies, it is important to understand which methods are best used in which setting, how the methods compare to one another, and what needs to be done to continue progress in this field. This paper reviews these methods broken down by data setting: aggregate-level data, federated learning, and individual participant-level data. We define the conditional average treatment effect and discuss differences between parametric and nonparametric estimators, and we list key assumptions, both those that are required within a single study and those that are necessary for data combination. After describing existing approaches, we compare and contrast them and reveal open areas for future research. This review demonstrates that there are many possible approaches for estimating treatment effect heterogeneity through the combination of datasets, but that there is substantial work to be done to compare these methods through case studies and simulations, extend them to different settings, and refine them to account for various challenges present in real data.

翻译：基于观测协变量估计处理效应，能够提升针对特定个体的治疗定制能力。有效实现此目标需应对潜在混杂因素，并具备充足数据以充分估计效应调节作用。近期研究涌现大量工作，利用多中心随机对照试验和/或观察性数据集估计处理效应异质性。随着评估多源研究中处理效应异质性的新方法不断涌现，理解何种方法在何种场景下最优、各方法间的比较关系，以及该领域持续发展所需推进的工作，变得至关重要。本文综述了按数据类型划分的方法：聚合层面数据、联邦学习及个体参与者层面数据。我们定义了条件平均处理效应，讨论了参数与非参数估计量之间的差异，并列出关键假设——既包括单研究必需假设，也包括数据整合所需假设。在描述现有方法后，我们对其进行比较与对比，并揭示未来研究的开放领域。本综述表明，通过数据集组合估计处理效应异质性的方法众多，但仍需通过案例研究与模拟比较这些方法、将其扩展至不同场景、并完善以应对真实数据中的各类挑战。