High-Dimensional Mediation Analysis Based on Additive Hazards Model for Survival Data

Yidan Cui; Chengwen Luo; Linghao Luo; Zhangsheng Yu

doi:10.3389/fgene.2021.771932

High-Dimensional Mediation Analysis Based on Additive Hazards Model for Survival Data

Front Genet. 2021 Dec 23:12:771932. doi: 10.3389/fgene.2021.771932. eCollection 2021.

Authors

Yidan Cui^{1

2}, Chengwen Luo³, Linghao Luo^{1

2}, Zhangsheng Yu^{1

2

4}

Affiliations

¹ Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.
² SJTU-Yale Joint Center for Biostatistics, Shanghai Jiao Tong University, Shanghai, China.
³ Public Laboratory, Taizhou Hospital of Zhejiang Province, Wenzhou Medical University, Linhai, Zhejiang, China.
⁴ Clinical Research Institute, Shanghai Jiao Tong University School of Medicine, Shanghai, China.

Abstract

Mediation analysis has been extensively used to identify potential pathways between exposure and outcome. However, the analytical methods of high-dimensional mediation analysis for survival data are still yet to be promoted, especially for non-Cox model approaches. We propose a procedure including "two-step" variable selection and indirect effect estimation for the additive hazards model with high-dimensional mediators. We first apply sure independence screening and smoothly clipped absolute deviation regularization to select mediators. Then we use the Sobel test and the BH method for indirect effect hypothesis testing. Simulation results demonstrate its good performance with a higher true-positive rate and accuracy, as well as a lower false-positive rate. We apply the proposed procedure to analyze DNA methylation markers mediating smoking and survival time of lung cancer patients in a TCGA (The Cancer Genome Atlas) cohort study. The real data application identifies four mediate CpGs, three of which are newly found.

Keywords: SIS; additive hazards model; high-dimensional mediators; mediation analysis; survival data.