Primary Submission Category: Mediation Analysis, Mechanisms
The Blessings of Multiple Mediators: Removing Unmeasured Confounding Bias via Factor Analysis
Authors: Kan Chen, Ruoyu Wang, Zhonghua Liu, Xihong Lin,
Presenting Author: Kan Chen*
Multiple mediation analysis aims to evaluate the indirect effect of an exposure on outcomes through mediators, as well as the direct effect through other pathways. Traditional methods for estimating mediation effects require the strong assumption of no unmeasured confounding between the outcome and the set of mediators. However, when the exposure and mediators are not randomized, unmeasured confounding among the exposure, mediators, and outcome can lead to biased estimates. In this work, we introduce a novel framework called FAMA (Factor Analysis-based Mediation Analysis) to address unmeasured confounding in multiple mediation analysis within a linear model setting. FAMA combines an omitted-variable bias approach with factor analysis to estimate natural indirect effects in the presence of unmeasured confounders. We validate the framework through theoretical analysis and simulation studies, demonstrating its effectiveness and robustness. Additionally, we applied FAMA to data from the U.S. Department of Veterans Affairs Normative Aging Study to detect DNA methylation CpG sites that mediate the effect of smoking on lung function. Our analysis identified multiple DNA methylation CpG sites that may mediate the effect of smoking on lung function and robust to unmeasured confounding bias. Notably, we observed effect sizes ranging from –0.18 to –0.79, with a false discovery rate controlled at 0.05. This includes CpG sites in the genes AHRR and F2RL3 in the presence of unmeasured bias