A Bayesian semi-parametric approach to causal mediation for longitudinal mediators and time-to-event outcomes with application to a cardiovascular disease cohort study

TL;DR

This paper introduces a Bayesian semi-parametric method using BART models to analyze causal mediation in longitudinal studies with time-to-event outcomes, addressing complex confounding and mediators.

Contribution

It develops a novel Bayesian approach for causal mediation analysis that handles longitudinal, time-varying confounders, mediators, and competing risks in observational cohort data.

Findings

Applied to ARIC study data on CVD medications and mortality

Successfully estimated direct and indirect effects of medications on CVD death

Demonstrated the method's ability to handle complex longitudinal and survival data

Abstract

Causal mediation analysis of observational data is an important tool for investigating the potential causal effects of medications on disease-related risk factors, and on time-to-death (or disease progression) through these risk factors. However, when analyzing data from a cohort study, such analyses are complicated by the longitudinal structure of the risk factors and the presence of time-varying confounders. Leveraging data from the Atherosclerosis Risk in Communities (ARIC) cohort study, 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 identify and estimate direct and indirect causal effects in the presence of a competing event. We apply our methods to assess how medication, prescribed to target cardiovascular disease (CVD) risk factors, affects the time-to-CVD death.