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

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  • Tzu‐Jung Huang
  • Zhonghua Liu
  • Ian W. McKeague

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.

Suggested Citation

  • Tzu‐Jung Huang & Zhonghua Liu & Ian W. McKeague, 2025. "Post‐selection inference for high‐dimensional mediation analysis with survival outcomes," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 52(2), pages 756-776, June.
  • Handle: RePEc:bla:scjsta:v:52:y:2025:i:2:p:756-776
    DOI: 10.1111/sjos.12770
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