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可提升针对个体定制治疗的能力。为此需有效处理潜在混杂因素，并积累充足数据以充分估计效应调节。近期大量研究关注如何利用多个随机对照试验和/或观测性数据集估计处理效应异质性。随着评估处理效应异质性的新方法不断涌现，亟需明确不同方法在何种场景下适用、彼此间效能对比以及该领域持续推进所需方向。本文按数据形态分类评述这些方法：聚合层级数据、联邦学习与个体参与者层级数据。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差异，并列出关键假设——既包括单一研究所需假设，也涵盖数据组合的必要条件。在描述现有方法后，我们进行了横向对比，揭示了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存在多种可行路径，但仍需通过案例研究与模拟实验系统对比这些方法、拓展其应用场景，并针对真实数据中的多重挑战加以精进。