Factor-Adjusted Multiple Testing for High-Dimensional Individual Mediation Effects

TL;DR

This paper introduces a novel factor-adjusted debiased testing framework for high-dimensional mediation analysis, effectively controlling false discovery rates under complex dependence among mediators.

Contribution

It proposes a new method that accounts for latent factor dependence in high-dimensional mediation analysis, improving inference accuracy and robustness.

Findings

FADMT controls FDR under complex dependence structures.

The method shows superior performance in simulations.

Applications demonstrate practical utility in genomics and finance.

Abstract

Identifying individual mediators is a central goal of high-dimensional mediation analysis, yet pervasive dependence among mediators can invalidate standard debiased inference and lead to substantial false discovery rate (FDR) inflation. We propose a Factor-Adjusted Debiased Mediation Testing (FADMT) framework that enables large-scale inference for individual mediation effects with FDR control under complex dependence structures. Our approach posits an approximate factor structure on the unobserved errors of the mediator model, extracts common latent factors, and constructs decorrelated pseudo-mediators for the subsequent inferential procedure. We establish the asymptotic normality of the debiased estimator and develop a multiple testing procedure with theoretical FDR control under mild high-dimensional conditions. By adjusting for latent factor induced dependence, FADMT also improves robustness to spurious associations driven by shared latent variation in observational studies. Extensive simulations demonstrate the superior finite-sample performance across a wide range of correlation structures. Applications to TCGA-BRCA multi-omics data and to China's stock connect study further illustrate the practical utility of the proposed method.