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.

Highlights

  • Lung cancer continues to be the most common cancer type worldwide with the highest (18%) death rate among all malignant tumors (Wild et al, 2020). Zeilinger et al (2013) found that tobacco smoking has an extensive genome-wide influence on DNA methylation

  • High-dimensional data analysis methods are becoming increasingly important with the development of sequence technologies

  • Mediation analysis is effective for identifying potential pathways

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Summary

Introduction

Lung cancer continues to be the most common cancer type worldwide with the highest (18%) death rate among all malignant tumors (Wild et al, 2020). Zeilinger et al (2013) found that tobacco smoking has an extensive genome-wide influence on DNA methylation. Tsou et al (2002) discovered that DNA methylation has a strong relationship with lung cancer. It is of interest to study how DNA methylation mediates the causal pathway between smoking and lung cancer patient’s survival. For potential indirect effects (IEs) detection, was first applied to psychological theory and research (Baron and Kenny, 1986). This idea was generally applied to sociological and biomedical fields (Kahler et al, 2017; Lapointe-Shaw et al, 2018; Vansteelandt et al, 2019; Arora et al, 2020; Song et al, 2020). The mediation model can be expressed in the following equations:

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