This week's biostatistics seminar speaker will be Dr. Lei Liu from the Division of Biostatistics at Washington University in St. Louis.

Abstract: Mediation analysis has become a prevalent technique to identify causal pathways between environmental exposures and health outcomes. However, little work has been done when the intermediate variables are high-dimensional and the outcome is a survival endpoint. In this paper, we introduce a novel method to identify potential mediators in the framework of high-dimensional Cox regression. We first reduce the data dimension through a mediation-based sure independence screening (SIS) method. A de-biased Lasso inference procedure is used for Cox regression parameters. We adopt a multiple-testing procedure to accurately control the false discovery rate (FDR) when testing high-dimensional mediation hypotheses. Simulation studies are conducted to demonstrate the performance of our method. We apply this approach to explore the causal mediation mechanisms of 379,330 DNA methylation markers between smoking and overall survival among lung cancer patients in the TCGA lung cancer cohort. Two methylation sites (cg08108679 and cg26478297) are identified as potential mediating epigenetic markers. Our proposed method is available with the R package HIMA at https://cran.r-project.org/web/packages/HIMA/.