British Journal of Medicine and Medical Research, ISSN: 2231-0614,Vol.: 5, Issue.: 8
Bayesian Joint Modelling of Disease Progression Marker and Time to Death Event of HIV/AIDS Patients under ART Follow-up
Gemeda Bedaso Buta1, Ayele Taye Goshu1 and Hailemichael M. Worku2 1School of Mathematical and Statistical Sciences, Hawassa University, Ethiopia.
2Department of Methods and Statistics, Leiden University, The Netherlands.
Gemeda Bedaso Buta1, Ayele Taye Goshu1 and Hailemichael M. Worku2
1School of Mathematical and Statistical Sciences, Hawassa University, Ethiopia.
(1) Chan Shen, Department of Biostatistics, MD Anderson Cancer Center, University of Texas, USA.
(1) Fasakin Kolawole, Department of Haematology, Federal Medical Centre, Ido Ekiti, Nigeria.
(2) Nlida Virginia Gmez, Medicine Department, University of Buenos Aires, Argentina.
(3) Mohammad Kamali, Rehabilitation Research Centre, Iran University of Medical Sciences, Iran.
(4) Shalini Malhotra, Microbiology, Delhi University, India.
Complete Peer review History: http://www.sciencedomain.org/review-history/6672
Objectives: To develop separate and joint statistical models in the Bayesian framework for longitudinal measurements and time to death event data of HIV/AIDS patients.
Study design: Longitudinal study.
Place and Duration of Study: The population of study includes all HIV/AIDS patients who had been under follow up of Antiretroviral Therapy (ART) from January 2006 to December 2012 at Shashemene Referral Hospital in Ethiopia.
Methodology: The posterior model was analyzed using Gibbs sampler by sampling from the distributions of the parameters given the data. Convergence of each sample was maintained.
Results: The results indicated that the joint model was not significant indicating that the CD4 count did not have significant effect on the patient’s survival time. The results of both the separate and joint analyses were consistent. The separate model was better interims of goodness of fitness than the joint model, while the final joint model was found to be simpler (less complex) model than the separate models. In the longitudinal sub-model, the predictors: linear time, squared time, sex, and tobacco addiction were statistically significant at 0.05 level of significance. For the survival submodel, knowledge of ART and condom use were significantly related with time to death.
Conclusion: The Bayesian Joint model provides results consistent with that of the separate models.
ART; Bayesian; CD4 count; Joint model; Longitudinal model; Survival model.
Full Article - PDF Page 1034-1043
DOI : 10.9734/BJMMR/2015/12907Review History Comments