Causal mediation analysis for longitudinal and survival data in continuous time using Bayesian non-parametric joint models

TL;DR

This paper introduces a Bayesian non-parametric joint modeling framework for causal mediation analysis in longitudinal and survival data, addressing irregular data collection and unobserved covariate measurements.

Contribution

It develops a novel joint modeling approach for continuous-time longitudinal and survival data using an enriched Dirichlet process mixture, enabling causal inference with incomplete covariate data.

Findings

Applied to ARIC study data to assess medication effects on CVD death.

Enabled inference at ages with missing covariate measurements.

Provided a flexible framework for causal mediation in complex longitudinal data.

Abstract

Observational cohort data is an important source of information for understanding the causal effects of treatments on survival and the degree to which these effects are mediated through changes in disease-related risk factors. However, these analyses are often complicated by irregular data collection intervals and the presence of longitudinal confounders and mediators. We propose a causal mediation framework that jointly models longitudinal exposures, confounders, mediators, and time-to-event outcomes as continuous functions of age. This framework for longitudinal covariate trajectories enables statistical inference even at ages where the subject's covariate measurements are unavailable. The observed data distribution in our framework is modeled using an enriched Dirichlet process mixture (EDPM) model. Using data from the Atherosclerosis Risk in Communities cohort study, we apply our methods to assess how medication -- prescribed to target cardiovascular disease (CVD) risk factors -- affects the time-to-CVD death.