Estimating heterogeneous treatment effects (HTEs) is crucial for precision medicine. While multiple studies can improve the generalizability of results, leveraging them for estimation is statistically challenging. Existing approaches often assume identical HTEs across studies, but this may be violated due to various sources of between-study heterogeneity, including differences in study design, study populations, and data collection protocols, among others. To this end, we propose a framework for multi-study HTE estimation that accounts for between-study heterogeneity in the nuisance functions and treatment effects. Our approach, the multi-study R-learner, extends the R-learner to obtain principled statistical estimation with machine learning (ML) in the multi-study setting. It involves a data-adaptive objective function that links study-specific treatment effects with nuisance functions through membership probabilities, which enable information to be borrowed across potentially heterogeneous studies. The multi-study R-learner framework can combine data from randomized controlled trials, observational studies, or a combination of both. It's easy to implement and flexible in its ability to incorporate ML for estimating HTEs, nuisance functions, and membership probabilities. In the series estimation framework, we show that the multi-study R-learner is asymptotically normal and more efficient than the R-learner when there is between-study heterogeneity in the propensity score model under homoscedasticity. We illustrate using cancer data that the proposed method performs favorably compared to existing approaches in the presence of between-study heterogeneity.

翻译：异质性治疗效果（HTE）的估计对精准医学至关重要。尽管多项研究可提升结果的泛化性，但利用多项研究进行估计在统计上具有挑战性。现有方法常假设研究间HTE相同，然而这一假设可能因研究设计、研究人群及数据收集方案等多种研究间异质性来源而被违背。为此，我们提出一个多研究HTE估计框架，该框架能够处理干扰函数与治疗效果中的研究间异质性。我们的方法——多研究R学习器——扩展了R学习器，以在多研究场景中利用机器学习（ML）实现具有理论依据的统计估计。该方法通过数据自适应目标函数，将特定研究的治疗效果与干扰函数通过隶属概率相关联，从而实现潜在异质性研究间的信息借取。多研究R学习器框架可整合随机对照试验、观察性研究或两者结合的数据。该框架易于实现，且能灵活融入ML进行HTE、干扰函数及隶属概率的估计。在级数估计框架下，我们证明当倾向评分模型存在研究间异质性且方差齐性时，多研究R学习器具有渐近正态性且比R学习器更高效。通过癌症数据的分析表明，在存在研究间异质性的情况下，所提方法优于现有方法。