Learning association from multiple intermediate events for dynamic prediction of survival: an application to cardiovascular disease prognosis

TL;DR

This paper introduces a copula-based framework for dynamic survival prediction in cardiovascular disease, modeling dependencies among multiple diseases and death, and demonstrating improved prediction accuracy.

Contribution

It develops a novel nonparametric, dependence-learning approach for survival analysis that accounts for multiple correlated events and informative censoring.

Findings

The method accurately estimates associations among disease onsets and death.

Simulation studies confirm the model's flexibility and predictive performance.

Application to heart disease data shows improved survival prediction by incorporating disease dependencies.

Abstract

Cardiovascular diseases are major causes of mortality globally. They often co-occur and are interrelated, leading to partial-order relationships among their onset times. However, these onset times are subject to informative censoring due to the occurrence of death, posing significant challenges for survival prediction. In this article, we propose a novel copula-based framework that learns dependence among multiple correlated marginal components through a pseudo-likelihood for estimation. We adopt nonparametric marginals, alleviating the reliance on marginal distribution assumptions typically required in conventional copula models, and estimate the association between the onsets of intermediate cardiovascular diseases and death by solving a concordance estimating equation. Under this framework, a renewable risk assessment method is developed for dynamic survival prediction, leveraging information on disease onset times and the maximum follow-up duration. Our proposed method yields estimators with well-established properties, and its flexibility and predictive effectiveness are demonstrated through extensive simulation studies. We apply the method to data from a heart disease study, demonstrating the benefits of incorporating the associations among various cardiovascular diseases and their synergistic effects on mortality for dynamic prediction of overall survival.