B0498
Title: A Bayesian semi-parametric approach to causal mediation for longitudinal mediators and time-to-event outcomes
Authors: Saurabh Bhandari - University of Florida (United States) [presenting]
Michael Joseph Daniels - University of Florida (United States)
Maria Josefsson - Umea School of Business, Economics and Statistics (Sweden)
Juned Siddique - Northwestern University (United States)
Abstract: Causal mediation analysis is a powerful tool for investigating the causal effects of medications on disease-related risk factors, and on time-to-death (or disease progression) through these risk factors. However, such analyses are complicated by the longitudinal structure of the risk factors and the time-varying confounders. A causal mediation approach is developed, using (semi-parametric) Bayesian additive regression tree (BART) models for the longitudinal and survival data. The framework allows for time-varying exposures, confounders, and mediators, all of which can either be continuous or binary. The method is also extended to quantify direct and indirect causal effects in the presence of a competing event. Using data from the atherosclerosis risk in communities (ARIC) cohort study, the methods are used to infer how medications, prescribed to target the cardiovascular disease (CVD) risk factors, affect the time-to-CVD death among 15,792 participants examined four times at three-year intervals.
