🤖 AI Summary
This study addresses limitations in existing meta-analytic methods for mediation, which often suffer from confounding bias, missing data, and ill-defined target populations, thereby undermining causal interpretation. To overcome these issues, the authors propose a causally interpretable meta-mediation framework that first transports natural indirect effects from individual studies to a clearly specified target population and leverages studies containing mediator variables—even those not originally designed for mediation analysis—to broaden the evidence base. The framework introduces a novel random-effects model and a nonparametric heterogeneity decomposition method based on ANOVA-type sums of squares, alongside a data-adaptive estimator grounded in semiparametric theory to flexibly accommodate confounding and missingness. Simulation and empirical analyses demonstrate that the proposed approach performs robustly in finite samples and substantially enhances both the causal interpretability and applicability of mediation effect meta-analysis.
📝 Abstract
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.