Post-selection inference for high-dimensional mediation analysis with survival outcomes
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866917747508641792 |
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| author | Huang, Tzu-Jung Liu, Zhonghua McKeague, Ian W. |
| author_facet | Huang, Tzu-Jung Liu, Zhonghua McKeague, Ian W. |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2408_06517 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Post-selection inference for high-dimensional mediation analysis with survival outcomes Huang, Tzu-Jung Liu, Zhonghua McKeague, Ian W. Methodology Statistics Theory 62N03 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. |
| title | Post-selection inference for high-dimensional mediation analysis with survival outcomes |
| topic | Methodology Statistics Theory 62N03 |
| url | https://arxiv.org/abs/2408.06517 |