A Bayesian semi-parametric approach to causal mediation for longitudinal and time-to-event data

Oregon Convention Center 

Causal mediation analysis is an important 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. We develop a causal mediation approach, using (semi-parametric) Bayesian Additive Regression Tree (BART) models for the longitudinal and survival data. Our framework allows for time-varying exposures, confounders, and mediators, all of which can either be continuous or binary. We also quantify direct and indirect causal effects in the presence of a competing event. Motivated by data from the Atherosclerosis Risk in Communities (ARIC) cohort study, we use our methods to assess how medications, prescribed to target the cardiovascular disease (CVD) risk factors, affect the time-to-CVD death.

causal mediation

longitudinal and survival data

semi-parametric

BART

cardiovascular 

Biometrics Section