Methods for Integrating Trials and Non-Experimental Data to Examine Treatment Effect Heterogeneity
整合试验与非实验数据研究处理效应异质性的方法AI 解读 ↓
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整合试验与非实验数据研究处理效应异质性的方法
- 研究问题
- 如何整合多个随机试验或观察数据集估计条件处理效应，并根据数据共享方式、混杂风险和样本信息选择方法？
- 方法与思路
- 作者按汇总数据、联邦学习和个体参与者数据分类综述，比较参数与非参数估计，并梳理单研究及跨研究假设。
- 材料陈述的贡献
- 摘要称系统对照了多数据集处理效应异质性方法，并指出案例与模拟比较、场景扩展及真实数据挑战处理仍需推进。
- 阅读关注点
- 应核对各方法的目标人群、研究间可比性和混杂控制要求，以及不同数据访问条件下方法比较是否充分。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未核实；它属于方法综述，摘要未给出完整假设清单或统一性能比较。
原文摘要
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.