Mediation analysis for survival data with high-dimensional mediators

Haixiang Zhang, Yinan Zheng, Lifang Hou, Cheng Zheng, Lei Liu

Research output: Contribution to journalArticlepeer-review

13 Scopus citations

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 studies are conducted to demonstrate the performance of our method. We apply this approach to explore the mediation mechanisms of 379 330 DNA methylation markers between smoking and overall survival among lung cancer patients in The Cancer Genome Atlas lung cancer cohort. Two methylation sites (cg08108679 and cg26478297) are identified as potential mediating epigenetic markers.

Original languageEnglish
Pages (from-to)3815-3821
Number of pages7
JournalBioinformatics
Volume37
Issue number21
DOIs
StatePublished - Nov 1 2021

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