Post‐selection inference for high‐dimensional mediation analysis with survival outcomes
针对高维中介变量场景，提出一种基于半参数有效影响函数的后选择推断方法，用于识别暴露对生存结局的边际中介效应，并在肺癌数据中发现多个DNA甲基化位点可能介导吸烟对肺癌生存的影响。
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.