Comparison of methods for mediation analysis with multiple correlated mediators

TL;DR

This paper compares six methods for mediation analysis with multiple correlated mediators, evaluating their performance through simulations and real data application to guide researchers in choosing appropriate techniques.

Contribution

It provides a comprehensive comparison of six mediation analysis methods for multiple correlated mediators, highlighting their strengths and limitations in various scenarios.

Findings

Different methods yield varying conclusions in real data analysis.

Simulation results show differences in bias and coverage among methods.

Guidance is provided for selecting suitable mediation analysis techniques.

Abstract

Various methods have emerged for conducting mediation analyses with multiple correlated mediators, each with distinct strengths and limitations. However, a comparative evaluation of these methods is lacking, providing the motivation for this paper. This study examines six mediation analysis methods for multiple correlated mediators that provide insights to the contributors for health disparities. We assessed the performance of each method in identifying joint or path-specific mediation effects in the context of binary outcome variables varying mediator types and levels of residual correlation between mediators. Through comprehensive simulations, the performance of six methods in estimating joint and/or path-specific mediation effects was assessed rigorously using a variety of metrics including bias, mean squared error, coverage and width of the 95$\%$ confidence intervals. Subsequently, these methods were applied to the REasons for Geographic And Racial Differences in Stroke (REGARDS) study, where differing conclusions were obtained depending on the mediation method employed. This evaluation provides valuable guidance for researchers grappling with complex multi-mediator scenarios, enabling them to select an optimal mediation method for their research question and dataset.