Mediation analysis for survival data with high-dimensional mediators.

ABSTRACT:

Motivation

Mediation analysis has become a prevalent method to identify causal pathway(s) between an independent variable and a dependent variable through intermediate variable(s). However, little work has been done when the intermediate variables (mediators) are high-dimensional and the outcome is a survival endpoint. In this paper, we introduce a novel method to identify potential mediators in a causal framework of high-dimensional Cox regression.

Results

We first reduce the data dimension through a mediation-based sure independence screening method. A de-biased Lasso inference procedure is used for Cox's regression parameters. We adopt a multiple-testing procedure to accurately control the false discovery rate when testing high-dimensional mediation hypotheses. Simulation

SUBMITTER: Zhang H 

PROVIDER: S-EPMC8570823 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature