Long-Term Survival of Patient with End-Stage Renal Disease Using Bayesian Mixture Cure Rate Frailty Models

Abbas  Bahrampour; Mohammad Reza  Baneshi; Ali  Karamoozian; Navisa Sadat  Seyedghasemi; Abbas  Etminan; Mostafa  Eghbalian

doi:10.18502/ijph.v53i9.16464

Abbas Bahrampour Department of Biostatistics and Epidemiology, Faculty of Health, Modeling in Health Research Center, Institute for Futures Stud-ies in Health, Kerman University of Medical Sciences, Kerman, Iran
Mohammad Reza Baneshi Australian Women and Girls’ Health Research Centre, School of Public Health, The University of Queensland, Herston, Queens-land, Australia
Ali Karamoozian Modeling in Health Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran
Navisa Sadat Seyedghasemi Department of Biostatistics and Epidemiology, Faculty of Health, Golestan University of Medical Sciences, Gorgan, Iran
Abbas Etminan Physiology Research Center, Departments of Nephrology, Urology and Renal Transplantation, Kerman University of Medical Scienc-es, Kerman, Iran
Mostafa Eghbalian Department of Biostatistics and Epidemiology, School of Public Health, Kerman University of Medical Sciences, Kerman, Iran

DOI: https://doi.org/10.18502/ijph.v53i9.16464

Keywords: Weibull distribution; Long-term survival; Mixture cure; Gamma frailty; Bayesian inference

Abstract

Background: Along with the increasing prevalence of ESRD in developing countries, the use of more up-to-date statistical models is highly recommended. It is crucial to control potential cure pattern and heterogenicity among patients.

Methods: In this longitudinal study, the data of 170 hemodialysis patients who visited the dialysis department of Shafa Hospital in Kerman from 2006 to 2016 were collected. To provides robust estimates the time to event data (death) were analyzed with a gamma frailty mixed cure Weibull model (MC-WG) using Bayesian inference.

Results: About 49% of patients experienced the death and median survival time was 37.5 months. Older patients (0.264), female patients (0.269), and patients with higher mean serum urea levels (0.186) had a higher risk of death. Moreover, we observe a decrease in death with increase in Creatine (Cr).

Conclusion: In the MC-WG Bayesian model, the diabetes, AST, calcium, phosphorus and uric acid variables had a significant effect on the survival of hemodialysis patients, while they were not significant in the Cox PH model. The results of MC-WG Bayesian model are more consistent with other studies.