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Joint modeling of recurrent events and survival: a Bayesian non-parametric approach
Giorgio Paulon1, Maria De Iorio2, Alessandra Guglielmi3
1Department of Statistics and Data Sciences, The University of Texas at Austin, 2317 Speedway (D9800), Austin, TX 78712-1823, USA.
Insights
This study introduces a joint statistical model to analyze how repeated heart failure (HF) hospitalizations impact patient survival time. The model accounts for individual patient differences and the correlation between hospitalizations and death.
Area of Science:
- Biostatistics
- Public Health
- Health Economics
Background:
- Heart failure (HF) significantly contributes to morbidity, hospitalization, and mortality in Western countries.
- The economic burden of HF management is substantial and projected to rise.
- Understanding factors influencing HF patient outcomes, particularly rehospitalizations, is crucial.
Purpose of the Study:
- To develop a joint statistical model for analyzing the time to death in heart failure patients.
- To investigate the impact of recurrent hospitalizations on survival time.
- To account for patient heterogeneity and correlations between recurrent events and survival.
Main Methods:
- Utilized hospitalization data from Lombardia, Italy's most populated region.
- Developed a joint model for gap times between rehospitalizations and survival time.
- Employed a Bayesian nonparametric Dirichlet process prior for a shared patient-specific frailty term to model heterogeneity.
- Incorporated dependent censoring and correlations between gap times.
- Implemented posterior inference using Markov chain Monte Carlo (MCMC) methods.
Main Results:
- The proposed joint model effectively integrates recurrent event data (gap times) with survival data.
- The model accounts for patient-specific frailty, capturing unobserved heterogeneity.
- It addresses dependent censoring and correlations between recurrent hospitalizations and survival.
- Covariates can be readily incorporated into both recurrence and survival processes.
Conclusions:
- The developed joint model provides a flexible and interpretable framework for analyzing recurrent events and survival data in heart failure.
- This methodology offers a robust approach to understanding the complex relationship between rehospitalizations and mortality in HF patients.
- The model's wide applicability, ease of interpretation, and computational efficiency make it valuable for clinical and health economic research.
Abstract:
Heart failure (HF) is one of the main causes of morbidity, hospitalization, and death in the western world, and the economic burden associated with HF management is relevant and expected to increase in the future. We consider hospitalization data for HF in the most populated Italian Region, Lombardia. Data were extracted from the administrative data warehouse of the regional healthcare system. The main clinical outcome of interest is time to death and research focus is on investigating how recurrent hospitalizations affect the time to event. The main contribution of the article is to develop a joint model for gap times between consecutive rehospitalizations and survival time. The probability models for the gap times and for the survival outcome share a common patient specific frailty term. Using a flexible Dirichlet process model for %Bayesian nonparametric prior as the random-effects distribution accounts for patient heterogeneity in recurrent event trajectories. Moreover, the joint model allows for dependent censoring of gap times by death or administrative reasons and for the correlations between different gap times for the same individual. It is straightforward to include covariates in the survival and/or recurrence process through the specification of appropriate regression terms. The main advantages of the proposed methodology are wide applicability, ease of interpretation, and efficient computations. Posterior inference is implemented through Markov chain Monte Carlo methods.
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