A Bayesian Nonparametric Approach for Semi-Competing Risks with Application to Cardiovascular Health

TL;DR

This paper introduces a Bayesian nonparametric method for causal inference in semi-competing risks, specifically applied to cardiovascular health, allowing flexible modeling of joint event times and treatment effects.

Contribution

It develops a novel Bayesian nonparametric framework using vine copulas and EDPM for semi-competing risks, with causal estimands and sensitivity analysis, applied to real health data.

Findings

Effective joint event-time estimation with minimal assumptions

Interpretable treatment effect estimates with credible intervals

Application to cardiovascular health study data

Abstract

We address causal estimation in semi-competing risks settings, where a non-terminal event may be precluded by one or more terminal events. We define a principal-stratification causal estimand for treatment effects on the non-terminal event, conditional on surviving past a specified landmark time. To estimate joint event-time distributions, we employ both vine-copula constructions and Bayesian nonparametric Enriched Dirichlet-process mixtures (EDPM), enabling inference under minimal parametric assumptions. We index our causal assumptions with sensitivity parameters. Posterior summaries via MCMC yield interpretable estimates with credible intervals. We illustrate the proposed method using data from a cardiovascular health study.