Thursday, January 17, 2019 - 3:00pm
209 W Eighteenth Ave (EA), Room 170
Regularized Multiple Mediation Analysis for High-Dimensional Data Sets
Qingzhao Yu, School of Public Health, George Washington University
Mediation analysis is used to explore the effect of a third variable (mediator) on an established exposure-outcome relationship. Multiple mediation analysis refers to the mediation analysis with multiple mediators. We propose to use the elastic net regularized linear regression in multiple mediation analysis when the number of potential mediators is large. In exploring the exposure-mediator-outcome relationship, we regularize coefficients of mediators in predicting the outcome. The penalization on the coefficient is inversely proportional to the association between the exposure variable and each mediator. Therefore, in estimating the effect of a mediator, the exposure-mediator and the mediator-outcome associations are jointly considered. An R package, mmabig, is compiled for the proposed method. We perform a series of sensitivity and specificity analysis to examine factors that can influence the power of identifying important mediators. Further, we illustrate how to consider potential nonlinear associations among variables in the mediation analysis. Simulation studies have shown that the proposed mediation analysis method consistently obtains larger power when compared with its main competitors. The method is used with a real data set to explore factors that contribute to the racial disparity in survival rates among breast-cancer patients diagnosed in 2004 in Louisiana.