Model-free High Dimensional Mediator Selection with False Discovery Rate Control

TL;DR

This paper introduces a model-free method for selecting high-dimensional mediators with FDR control, suitable for complex correlation structures, and demonstrates its effectiveness through simulations and real ADNI data analysis.

Contribution

It develops a novel hypothesis testing framework for high-dimensional mediator selection with FDR control under mild assumptions.

Findings

Achieves theoretical FDR control.

Demonstrates high power in simulations.

Identifies key brain regions mediating gender and dementia progression.

Abstract

There is a challenge in selecting high-dimensional mediators when the mediators have complex correlation structures and interactions. In this work, we frame the high-dimensional mediator selection problem into a series of hypothesis tests with composite nulls, and develop a method to control the false discovery rate (FDR) which has mild assumptions on the mediation model. We show the theoretical guarantee that the proposed method and algorithm achieve FDR control. We present extensive simulation results to demonstrate the power and finite sample performance compared with existing methods. Lastly, we demonstrate the method for analyzing the Alzheimer's Disease Neuroimaging Initiative (ADNI) data, in which the proposed method selects the volume of the hippocampus and amygdala, as well as some other important MRI-derived measures as mediators for the relationship between gender and dementia progression.