Mediation analysis for survival data with high-dimensional mediators | Zendy

Haixiang Zhang | Zendy; Yinan Zheng | Zendy; Lifang Hou | Zendy; Cheng Zheng | Zendy; Lei Liu | Zendy

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Mediation analysis for survival data with high-dimensional mediators

Author(s) -

Haixiang Zhang,

Yinan Zheng,

Lifang Hou,

Cheng Zheng,

Lei Liu

Publication year - 2021

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btab564

Subject(s) - mediation , variable (mathematics) , proportional hazards model , outcome (game theory) , variables , regression analysis , causal inference , econometrics , computer science , instrumental variable , regression , causal model , psychology , statistics , mathematics , machine learning , sociology , mathematical analysis , social science , mathematical economics

Mediation analysis has become a prevalent method to identify causal pathway(s) between an independent variable and a dependent variable through intermediate variable(s). However, little work has been done when the intermediate variables (mediators) are high-dimensional and the outcome is a survival endpoint. In this paper, we introduce a novel method to identify potential mediators in a causal framework of high-dimensional Cox regression.

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