Bayesian competing risks survival modeling for assessing the cause of death of patients with heart failure

TL;DR

This paper introduces a Bayesian competing risks survival model to analyze causes of death in heart failure patients, incorporating covariates and model selection, with applications to cardiac resynchronization therapy outcomes.

Contribution

It develops a Bayesian framework for competing risks survival analysis, including model selection and checking procedures, applied to heart failure patient data.

Findings

Identified key covariates influencing cause-specific mortality.

Computed posterior transition probabilities for different causes of death.

Validated the model's effectiveness in a clinical setting.

Abstract

Competing risk models are survival models with several events of interest acting in competition and whose occurrence is only observed for the event that occurs first in time. This paper presents a Bayesian approach to these models in which the issue of model selection is treated in a special way by proposing generalizations of some of the Bayesian procedures used in univariate survival analysis. This research is motivated by a study on the survival of patients with hearth failure undergoing cardiac resynchronization therapy, a procedure which involves the implant of a device to stabilize the heartbeat. Two different causes of causes of death have been considered: cardiovascular and non-cardiovascular, and a set of baseline covariates are examined in order to better understand their relationship with both causes of death. Model selection procedures and model checking analyses have been implemented and assessed. The posterior distribution of some relevant outputs such as transition probabilities have been computed and discussed.