American Journal of Theoretical and Applied Statistics

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Bayesian Joint Modelling of Longitudinal and Survival Data of HIV/AIDS Patients: A Case Study at Bale Robe General Hospital, Ethiopia

Received: 14 February 2017    Accepted: 25 February 2017    Published: 23 June 2017
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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.

DOI 10.11648/j.ajtas.20170604.13
Published in American Journal of Theoretical and Applied Statistics (Volume 6, Issue 4, July 2017)
Page(s) 182-190
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

ART, Bayesian, CD4 Count, HIV/AIDS, Joint Model, Longitudinal Model, Survival Model

References
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[11] Ibrahim JG, Chen MH, Sinha D. Bayesian methods for joint modeling of longitudinal and survival data with applications to cancer vaccine trials. Statistica Sinica. 2004 Jul 1: 863-83.
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Author Information
  • Department of Statistics, College of Natural and Computational Science, Madda Walabu University, Bale Robe, Ethiopia

  • School of Mathematical and Statistical Sciences, Hawassa University, Hawassa, Ethiopia

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    Ahmed Hasan Dessiso, Ayele Taye Goshu. (2017). 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, 6(4), 182-190. https://doi.org/10.11648/j.ajtas.20170604.13

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    ACS Style

    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. Am. J. Theor. Appl. Stat. 2017, 6(4), 182-190. doi: 10.11648/j.ajtas.20170604.13

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    AMA Style

    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. Am J Theor Appl Stat. 2017;6(4):182-190. doi: 10.11648/j.ajtas.20170604.13

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  • @article{10.11648/j.ajtas.20170604.13,
      author = {Ahmed Hasan Dessiso and Ayele Taye Goshu},
      title = {Bayesian Joint Modelling of Longitudinal and Survival Data of HIV/AIDS Patients: A Case Study at Bale Robe General Hospital, Ethiopia},
      journal = {American Journal of Theoretical and Applied Statistics},
      volume = {6},
      number = {4},
      pages = {182-190},
      doi = {10.11648/j.ajtas.20170604.13},
      url = {https://doi.org/10.11648/j.ajtas.20170604.13},
      eprint = {https://download.sciencepg.com/pdf/10.11648.j.ajtas.20170604.13},
      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.},
     year = {2017}
    }
    

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    AU  - Ahmed Hasan Dessiso
    AU  - Ayele Taye Goshu
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    AB  - 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.
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