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

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.

Instrumental variable-based high-dimensional mediation analysis with unmeasured confounders for survival data in the observational epigenetic study

“… Zhang et al (2016) raised the issue of estimating the high-dimensional mediating effect in survival analysis ( Zhang et al, 2016 ). Gao et al (2019) , Luo et al (2020) , and Zhang et al (2021a) all proposed high-dimensional mediating analysis approaches based on penalty methods, and Cui et al (2021) proposed a high-dimensional mediation analysis approach for survival data based on the addictive hazard model ( Gao et al, 2019 ; Luo et al, 2020 ; Zhang et al, 2021b ; Cui et al, 2021 ). These approaches have provided useful statistical tools for practical analysis; however, the issue of confounders remained ( Stuart et al, 2021 ).…”

Linear high-dimensional mediation models adjusting for confounders using propensity score method

“…Many scholars have focused on the hypothesis testing method under high-dimension cases ( Huang and Pan, 2016 ; Djordjilovic et al, 2019 ; Gao et al, 2019 ); while for the mediator selection problem, Zhang et al first proposed a complete high-dimensional mediation analysis (HIMA) model based on SIS dimension reduction, MCP penalty estimation, and joint-significance test ( Zhang et al, 2016 ). Furthermore, HIMA was generalized to survival outcome and non-linear assumptions for different application scenarios ( Loh et al, 2020 ; Luo et al, 2020 ; Cui et al, 2021 ; Zhang et al, 2021 ).…”

High-Dimensional Mediation Analysis for Time-to-Event Outcomes with Additive Hazards Model

“…In this section, we conduct some simulations under two different types of scenarios (binary and continuous exposure) to evaluate the performance of our two proposed approaches, where the ones mentioned in the previous Sections 2 and 3 are denoted as 'HDMT' and 'Knockoff', respectively. We also compare with the one in paper [23], denoted as 'Cui'. Similar to [22], we choose δ 0 = 0.2 for 'Knockoff' in the simulations.…”

“…[22] introduced a novel approach that incorporates the aggregation of multiple knockoffs into the Cox model for analyzing survival outcomes with high-dimensional mediators. The current literature on high-dimensional mediation analysis of additive hazard models for survival data is limited, with the exception of [23]. This kind of high-dimensional topic also has potential applications in other fields [24][25][26][27][28].…”