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

Ahmed Hasan Dessiso; Ayele Taye Goshu

doi:doi:10.11648/j.ajtas.20170604.13

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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

Ahmed Hasan Dessiso, Ayele Taye Goshu

Published in American Journal of Theoretical and Applied Statistics (Volume 6, Issue 4)

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.

Published in	American Journal of Theoretical and Applied Statistics (Volume 6, Issue 4)
DOI	10.11648/j.ajtas.20170604.13
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), 2017. Published by Science Publishing Group

Keywords

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

References

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APA Style

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://article.sciencepublishinggroup.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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TY  - JOUR
T1  - Bayesian Joint Modelling of Longitudinal and Survival Data of HIV/AIDS Patients: A Case Study at Bale Robe General Hospital, Ethiopia
AU  - Ahmed Hasan Dessiso
AU  - Ayele Taye Goshu
Y1  - 2017/06/23
PY  - 2017
N1  - https://doi.org/10.11648/j.ajtas.20170604.13
DO  - 10.11648/j.ajtas.20170604.13
T2  - American Journal of Theoretical and Applied Statistics
JF  - American Journal of Theoretical and Applied Statistics
JO  - American Journal of Theoretical and Applied Statistics
SP  - 182
EP  - 190
PB  - Science Publishing Group
SN  - 2326-9006
UR  - https://doi.org/10.11648/j.ajtas.20170604.13
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.
VL  - 6
IS  - 4
ER  -

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Author Information

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

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APA Style

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://article.sciencepublishinggroup.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}
}

Copy | Download

TY  - JOUR
T1  - Bayesian Joint Modelling of Longitudinal and Survival Data of HIV/AIDS Patients: A Case Study at Bale Robe General Hospital, Ethiopia
AU  - Ahmed Hasan Dessiso
AU  - Ayele Taye Goshu
Y1  - 2017/06/23
PY  - 2017
N1  - https://doi.org/10.11648/j.ajtas.20170604.13
DO  - 10.11648/j.ajtas.20170604.13
T2  - American Journal of Theoretical and Applied Statistics
JF  - American Journal of Theoretical and Applied Statistics
JO  - American Journal of Theoretical and Applied Statistics
SP  - 182
EP  - 190
PB  - Science Publishing Group
SN  - 2326-9006
UR  - https://doi.org/10.11648/j.ajtas.20170604.13
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.
VL  - 6
IS  - 4
ER  -

Copy | Download