AI 中文总结
针对标准中介元分析的局限，提出新方法将特定研究的自然间接效应估计转移到目标人群，能整合相关研究，构建灵活估计器并开发新模型，通过模拟和真实数据评估有限样本性能，提升了因果可解释性和元中介分析的有效性。
AI 中文摘要
元分析自然间接效应估计越来越多地用于综合感兴趣的因果途径的证据。然而，标准的中介元分析方法通常基于结构方程模型，存在无法解决中介-结果混杂、不易扩展以处理缺失数据以及不清楚汇总间接效应所适用的目标人群等问题。本文提出一种新方法，在证据合成前将特定研究的自然间接效应估计转移到明确的目标人群，能整合未明确研究中介但收集了中介数据的研究以提高广泛性，还构建了灵活的数据自适应估计器，开发了新的随机效应模型等，通过模拟和真实数据评估了方法的有限样本性能。
英文摘要
Meta-analyzing natural indirect effect estimates from multiple studies is increas- ingly used to synthesize evidence on causal pathways of interest. However, stan- dard mediation meta-analysis approaches are typically based on structural equation modeling, which fails to account for mediator-outcome confounding, is not read- ily extended to address missing mediator and outcome data, and is often unclear about the target population to which the summary indirect effect pertains. In this work, we propose a novel method that addresses these limitations. Our ap- proach transports study-specific natural indirect effect estimates to a well-defined target population prior to evidence synthesis. The proposed methods enable the integration of studies that do not explicitly investigate mediation but collect data on the mediator to improve extensiveness. Using semiparametric theory, we con- struct flexible, data-adaptive estimators for the target parameter. Novel random- effects models and non-parametric analogues based on ANOVA sums of squares are also developed to decompose between-study heterogeneity into distinct sources that may affect the causal interpretability of the obtained findings. Finite-sample per- formance of the proposed methods is evaluated through simulated and real-world data.
