Individualized Dynamic Mediation Analysis Using Latent Factor Models

TL;DR

This paper introduces a novel individualized dynamic mediation analysis method that captures time-varying, heterogeneous mediation effects at the individual level, addressing limitations of static, population-level approaches.

Contribution

It proposes a varying-coefficient structural equation model that identifies significant mediators and accounts for unmeasured confounders, with proven asymptotic properties.

Findings

Effective mediator selection at individual level

Captures heterogeneity and time-varying effects

Demonstrated success in DNA methylation study

Abstract

Mediation analysis plays a crucial role in causal inference as it can investigate the pathways through which treatment influences outcome. Most existing mediation analysis assumes that mediation effects are static and homogeneous within populations. However, mediation effects usually change over time and exhibit significant heterogeneity among individuals in many real-world applications. Additionally, the mediation mechanism can be complicated and involves non-sparse, making mediator selection particularly challenging. To address these issues, we propose an individualized dynamic mediation analysis method for mediator selection. Our approach can identify the significant mediators at the population level while capturing the time-varying and heterogeneous mediation effects at the individual level via varying-coefficient structural equation models. Another advantage of our method is that we allow the presence of unmeasured time-varying confounders that induce the heterogeneous mediation effects. We provide asymptotic results for the proposed estimator and selection consistency for significant mediators. Extensive simulation studies and an application to a DNA methylation study demonstrate the effectiveness and advantages of our method.