Abstract:
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Environmental exposures such as cigarette smoking influence health outcomes through intermediate molecular phenotypes, such as the methylome, transcriptome, and metabolome. Mediation analysis is a useful tool for investigating the role of potentially high-dimensional intermediate phenotypes in the relationship between environmental exposures and health outcomes. Although researchers have recently made progress in high-dimensional mediation analysis for continuous outcomes in the linear model setting, considerable challenges remain for survival data in variable selection, censoring, and estimation of the total mediation effect. To this end, we propose an estimation procedure for Mediation Analysis of Survival outcome and High-dimensional omics mediators (MASH) based on sure independence screening for putative mediator variable selection and R2-like measure of total mediation effect for survival data analogous to the R2 measure in a linear model. Extensive simulations showed good performance of MASH in terms of bias, variance, and identifying true mediators.
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