Multi-Study R-Learner for Estimating Heterogeneous Treatment Effects Across Studies Using Statistical Machine Learning
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跨研究异质性处理效应估计的多研究 R-learner
- 研究问题
- 当不同研究的处理效应、倾向得分及其他干扰函数存在差异时，如何借用多项研究的信息估计异质性处理效应，而不强制效应相同？
- 方法与思路
- 作者扩展 R-learner，通过研究归属概率构造数据自适应目标函数，连接研究特定效应与干扰函数，并允许机器学习估计这些组成部分。
- 材料陈述的贡献
- 摘要称，框架可整合随机试验与观察研究；在级数估计、同方差且倾向得分存在研究间异质性的设定下，给出渐近正态性和相对效率结果。
- 阅读关注点
- 应核对研究归属概率如何实现信息借用、研究间可比性与各研究识别条件，以及级数估计下渐近正态性和相对 R-learner 效率结论的适用范围。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未经核实；摘要未列出完整识别与收敛条件，效率结论具有明确设定限制，癌症应用也未提供比较数值。
原文摘要
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.