Post-selection inference for high-dimensional mediation analysis with survival outcomes

TL;DR

This paper introduces a novel post-selection inference method for high-dimensional mediation analysis with survival outcomes, enabling valid identification of mediators in genomic data.

Contribution

It develops a semiparametric efficient influence function-based procedure for valid inference after mediator selection in high-dimensional settings.

Findings

Method performs well in simulations

Identifies potential DNA methylation mediators in lung cancer data

Provides valid statistical inference post mediator selection

Abstract

It is of substantial scientific interest to detect mediators that lie in the causal pathway from an exposure to a survival outcome. However, with high-dimensional mediators, as often encountered in modern genomic data settings, there is a lack of powerful methods that can provide valid post-selection inference for the identified marginal mediation effect. To resolve this challenge, we develop a post-selection inference procedure for the maximally selected natural indirect effect using a semiparametric efficient influence function approach. To this end, we establish the asymptotic normality of a stabilized one-step estimator that takes the selection of the mediator into account. Simulation studies show that our proposed method has good empirical performance. We further apply our proposed approach to a lung cancer dataset and find multiple DNA methylation CpG sites that might mediate the effect of cigarette smoking on lung cancer survival.