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Volume 6, Issue 4, July 2017, Page: 182-190
Bayesian Joint Modelling of Longitudinal and Survival Data of HIV/AIDS Patients: A Case Study at Bale Robe General Hospital, Ethiopia
Ahmed Hasan Dessiso, Department of Statistics, College of Natural and Computational Science, Madda Walabu University, Bale Robe, Ethiopia
Ayele Taye Goshu, School of Mathematical and Statistical Sciences, Hawassa University, Hawassa, Ethiopia
Received: Feb. 14, 2017;       Accepted: Feb. 25, 2017;       Published: Jun. 23, 2017
DOI: 10.11648/j.ajtas.20170604.13      View  2225      Downloads  140
Abstract
Joint analysis of longitudinal and survival data has received increasing attention in the recent years, especially for AIDS. This study explores application of Bayesian joint modeling of HIV/AIDS data obtained from Bale Robe General Hospital, Ethiopia. The objective is to develop separate and joint statistical models in the Bayesian framework for longitudinal measurements and time to death event data of HIV/AIDS patients. A linear mixed effects model (LMEM), assuming homogenous and heterogeneous CD4 variances, is used for modeling the CD4 counts and a Weibull survival model is used for describing the time to death event. Then, both processes are linked using unobserved random effects through the use of a shared parameter model. The analysis of both the separate and the joint models reveal that the assumption of heterogeneous (patient-specific) CD4 variances brings improvement in the model fit. The Bayesian joint model is found to best fit to the data, and provided more precise estimates of parameters. The shared frailty is significant showing the association between the linear mixed effect (LME) and survival models.
Keywords
ART, Bayesian, CD4 Count, HIV/AIDS, Joint Model, Longitudinal Model, Survival Model
To cite this article
Ahmed Hasan Dessiso, Ayele Taye Goshu, Bayesian Joint Modelling of Longitudinal and Survival Data of HIV/AIDS Patients: A Case Study at Bale Robe General Hospital, Ethiopia, American Journal of Theoretical and Applied Statistics. Vol. 6, No. 4, 2017, pp. 182-190. doi: 10.11648/j.ajtas.20170604.13
Copyright
Copyright © 2017 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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