Evidence map›Paper›PMID 39429658›Full record

ArticleIranian journal of public health2024

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

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Article in Iranian journal of public health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Abbas BahrampourDepartment of Biostatistics and Epidemiology, Faculty of Health, Modeling in Health Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran.
Mohammad Reza BaneshiAustralian Women and Girls' Health Research Centre, School of Public Health, The University of Queensland, Herston, Queensland, Australia.
Ali KaramoozianModeling in Health Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran.
Navisa Sadat SeyedghasemiDepartment of Biostatistics and Epidemiology, Faculty of Health, Golestan University of Medical Sciences, Gorgan, Iran.
Abbas EtminanPhysiology Research Center, Departments of Nephrology, Urology and Renal Transplantation, Kerman University of Medical Sciences, Kerman, Iran.
Mostafa EghbalianDepartment of Biostatistics and Epidemiology, School of Public Health, Kerman University of Medical Sciences, Kerman, Iran.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Bayesian inferenceGamma frailtyLong-term survivalMixture cureWeibull distribution

Identifiers

PMID39429658
PMCPMC11490321

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