High-dimensional mediation analysis for survival data

Mediation analysis with high-dimensional DNA methylation markers is critical for uncovering epigenetic pathways linking environmental exposures to health outcomes. While methodological advances have enabled mediation analysis with high-dimensional mediators for time-to-event data, existing approaches fail to account for joint mediator effects adequately. This limitation hinders the identification of mediators that are jointly dependent but marginally independent of the outcome. We propose a novel high-dimensional mediation analysis framework within a causal Cox proportional hazards model to address this gap. The proposed model integrates a non-marginal sure screening technique, a minimum approximated information criterion penalty for variable selection, and an adaptive bootstrap testing procedure to assess indirect effects. Simulation studies show that the proposed method performs satisfactorily. Applied to The Cancer Genome Atlas (TCGA) lung cancer cohort study, the proposed approach identifies candidate methylation markers with potential mediation roles in the association between tobacco smoking and reduced survival in lung cancer patients.