Ashine, T.; Likassa, H.; Chen, D.-G. Estimating Time-to-Death and Determining Risk Predictors for Heart Failure Patients: Bayesian AFT Shared Frailty Models Using INLA Method. Preprints2024, 2024081565. https://doi.org/10.20944/preprints202408.1565.v1
APA Style
Ashine, T., Likassa, H., & Chen, D. G. (2024). Estimating Time-to-Death and Determining Risk Predictors for Heart Failure Patients: Bayesian AFT Shared Frailty Models Using INLA Method. Preprints. https://doi.org/10.20944/preprints202408.1565.v1
Chicago/Turabian Style
Ashine, T., Habte Likassa and Ding-Geng Chen. 2024 "Estimating Time-to-Death and Determining Risk Predictors for Heart Failure Patients: Bayesian AFT Shared Frailty Models Using INLA Method" Preprints. https://doi.org/10.20944/preprints202408.1565.v1
Abstract
Heart failure is a major global health concern, especially in Ethiopia. Numerous studies have analyzed heart failure data to inform decision-making, but these often struggle with limitations to accurately capture death dynamics and account for within-cluster dependence and heterogeneity.
Addressing these limitations, this study is then to incorporate the dependence and to analyze heart
failure data to estimate survival time and identify risk factors affecting patient survival. The data, obtained from 497 patients at Jimma University Medical Center in Ethiopia, were collected between July 2015 and January 2019. Residence was considered as the clustering factor in the analysis. We employed the Bayesian accelerated failure time (AFT), and Bayesian AFT shared gamma frailty models, comparing their performance using the Deviance Information Criterion (DIC) and Watanabe-Akaike Information Criterion (WAIC). The Bayesian log-normal AFT shared gamma frailty model had the lowest DIC andWAIC, with well capturing cluster dependency that attributed to unobserved heterogeneity between patient residences. Unlike other methods that use Markov-Chain Monte-Carlo (MCMC), we applied the Integrated Nested Laplace Approximation (INLA) to reduce computational load. The study found that 39.44% of patients died, while 60.56% were censored, with a median survival time of 34 months. Another interesting finding of this study is by adding frailty into the Bayesian AFT models has boosted the performance in fitting the heart failure dataset. Significant factors reducing survival time included age, chronic kidney disease, heart failure history, diabetes, heart failure etiology, hypertension, anemia, smoking, and heart failure stage. The Bayesian model also showed lower standard errors for parameters compared to other methods, highlighting the importance of frailty models in accurately assessing survival time in heart failure patients.
Public Health and Healthcare, Public Health and Health Services
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
