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

Abstract

Mediation analysis plays an increasingly crucial role in identifying potential causal pathways between exposures and outcomes. However, there is currently a lack of developed mediation approaches for high-dimensional survival data, particularly when considering additive hazard models. The present study introduces two novel approaches for identifying statistically significant mediators in high-dimensional additive hazard models, including the multiple testing-based mediator selection method and knockoff filter procedure. The simulation results demonstrate the outstanding performance of these two proposed methods. Finally, we employ the proposed methodology to analyze the Cancer Genome Atlas (TCGA) cohort in order to identify DNA methylation markers that mediate the association between smoking and survival time among lung cancer patients.