Nonparametric estimators of interventional (transported) direct and indirect effects that accommodate multiple mediators and multiple intermediate confounders
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容许多个中介与多个中间混杂因素的干预型及可迁移直接、间接效应非参数估计
- 研究问题
- 当中介和暴露后的中间混杂因素都为多维时，如何估计干预型直接与间接效应，并将这些效应迁移到新的目标总体？
- 方法与思路
- 作者扩展两种已有非参数估计量，使其同时容许多维中介与多维中间混杂因素，并以模拟和纵向试验数据开展分析。
- 材料陈述的贡献
- 摘要称填补同时处理两类多维变量的估计缺口，并在 Moving to Opportunity 试验中展示按中介或中介组拆分间接效应的策略。
- 阅读关注点
- 应核对干预型与迁移效应的定义、识别条件及辅助模型要求，以及拆分间接效应时如何处理共现的中间变量。
- 阅读边界
- 所给版本为 arXiv 预印本，同行评审或期刊接收未经核实；摘要未说明完整识别假设、估计步骤及有限样本结果的具体幅度。
原文摘要
Mediation analysis is appealing for its ability to improve understanding of the mechanistic drivers of causal effects, but real-world data complexities challenge its successful implementation, including: 1) the existence of post-exposure variables that also affect mediators and outcomes (thus, confounding the mediator-outcome relationship), that may also be 2) multivariate, and 3) the existence of multivariate mediators. Interventional direct and indirect effects (IDE/IIE) accommodate post-exposure variables that confound the mediator-outcome relationship, but currently, no estimator for IDE/IIE exists that allows for both multivariate mediators and multivariate post-exposure intermediate confounders. This, again, represents a significant limitation for real-world analyses. We address this gap by extending two recently developed nonparametric estimators -- one that estimates the IDE/IIE and another that estimates the IDE/IIE transported to a new, target population -- to allow for multivariate mediators and multivariate intermediate confounders simultaneously. We use simulation to examine finite sample performance, and apply these estimators to longitudinal data from the Moving to Opportunity trial. In the application, we walk through a strategy for separating indirect effects into mediator- or mediator-group-specific indirect effects, while appropriately accounting for other, possibly co-occurring intermediate variables.