Group lasso based selection for high-dimensional mediation analysis

TL;DR

This paper introduces a two-step high-dimensional mediation analysis method combining group lasso-based selection with effect estimation, addressing correlated mediators and high-dimensional data challenges.

Contribution

It proposes a novel two-step procedure that effectively selects mediators and estimates mediated effects in high-dimensional settings with correlated variables.

Findings

Outperforms existing methods in simulated data scenarios.

Successfully applied to real data on DNA methylation, smoking, and rheumatoid arthritis.

Demonstrates robustness in high-dimensional, correlated mediator contexts.

Abstract

Mediation analysis aims to identify and estimate the effect of an exposure on an outcome that is mediated through one or more intermediate variables. In the presence of multiple intermediate variables, two pertinent methodological questions arise: estimating mediated effects when mediators are correlated, and performing high-dimensional mediation analyses when the number of mediators exceeds the sample size. This paper presents a two-step procedure for high-dimensional mediation analyses. The first step selects a reduced number of candidate mediators using an ad-hoc lasso penalty. The second step applies a procedure we previously developed to estimate the mediated effects, accounting for the correlation structure among the retained candidate mediators. We compare the performance of the proposed two-step procedure with state-of-the-art methods using simulated data. Additionally, we demonstrate its practical application by estimating the causal role of DNA methylation in the pathway between smoking and rheumatoid arthritis using real data.