Ahmed Hasan - Academia.edu (original) (raw)
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Papers by Ahmed Hasan
Objective: This paper aims to compare various Bayesian joint models based on the accelerated fail... more Objective: This paper aims to compare various Bayesian joint models based on the accelerated failure time distributions in analyzing longitudinal observations on CD4 cell counts as growth measurements and time-to-death events of HIV/AIDS patients. Three accelerated failure time distributions, namely, Weibull, lognormal and loglogistic distributions are considered. Methods: We consider a total of 354 random sample of HIV/AIDS patients who had been under ART follow-up at Shashemene Referral Hospital in Ethiopia from January 2006 to December 2012. Linear mixed effects model is used for the longitudinal outcomes (square root of CD4 cell counts) with normality assumption, while three parametric accelerated failure time distributions are studied for the time-to-event data. The Bayesian joint models are defined with association parameters and analyzed using Gibbs sampler algorithm. Non-informative prior distributions are assumed. The model selection criteria DIC is employed to identify the model with best fit to the data. Another data Original Research Article
Joint analysis of longitudinal and survival data has received increasing attention in the recent ... more 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.
Objective: This paper aims to compare various Bayesian joint models based on the accelerated fail... more Objective: This paper aims to compare various Bayesian joint models based on the accelerated failure time distributions in analyzing longitudinal observations on CD4 cell counts as growth measurements and time-to-death events of HIV/AIDS patients. Three accelerated failure time distributions, namely, Weibull, lognormal and loglogistic distributions are considered. Methods: We consider a total of 354 random sample of HIV/AIDS patients who had been under ART follow-up at Shashemene Referral Hospital in Ethiopia from January 2006 to December 2012. Linear mixed effects model is used for the longitudinal outcomes (square root of CD4 cell counts) with normality assumption, while three parametric accelerated failure time distributions are studied for the time-to-event data. The Bayesian joint models are defined with association parameters and analyzed using Gibbs sampler algorithm. Non-informative prior distributions are assumed. The model selection criteria DIC is employed to identify the model with best fit to the data. Another data Original Research Article
Joint analysis of longitudinal and survival data has received increasing attention in the recent ... more 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.