Bayesian Joint Modelling of Survival of HIV/AIDS Patients Using Accelerated Failure Time Data and Longitudinal CD4 Cell Counts

Erango, Markos Abiso; Goshu, Ayele Taye; Buta, Gemeda; Dessiso, Ahmed Hasan

doi:10.9734/bjmmr/2017/32123

Cited by 15 publications

(18 citation statements)

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“…As a result, the proposed joint models are estimated under a Bayesian framework using Markov chain Monte Carlo (MCMC) methods with Gibbs sampling using Win BUGS software. Various authors, including [10], [11], [12], [13], [14] and [15] have also studied Bayesian joint models. Joint models may contain many unknown parameters, which may lead to potential problems in inference.…”

Section: Bayesian Joint Model Parameter Estimationmentioning

confidence: 99%

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

Dessiso¹

2017

AJTAS

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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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Section: Bayesian Joint Model Parameter Estimationmentioning

confidence: 99%

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

Dessiso¹

2017

AJTAS

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“…Under AFT models, we measure the direct effect of the explanatory variables on the survival time instead that of the hazard [3], [4], [6].…”

Section: Aft Modelsmentioning

confidence: 99%

“…Loglogistic and lognormal distributions have hazard rate functions that are non-monotonic that is increasing to reach a peak and then declining over time [3], [4], [6] Weibull distribution with survival and hazard functions are:…”

Section: Aft Modelsmentioning

confidence: 99%

“…These observed biomarker series are frequently important health indicators that represent the progression of a disease. Such data typically have additional features and complications associated with them, including the presence of treatment group indicators and baseline covariates, measurement error in the biomarkers, and right censoring of the event time with the possibility of dependent censoring [3][4]. A study by [4] has demonstrated joint modelling of longitudinal observation of CD4 counts and time-to-death using AFT models under Bayesian settings.…”

Section: Introductionmentioning

confidence: 99%

“…Such data typically have additional features and complications associated with them, including the presence of treatment group indicators and baseline covariates, measurement error in the biomarkers, and right censoring of the event time with the possibility of dependent censoring [3][4]. A study by [4] has demonstrated joint modelling of longitudinal observation of CD4 counts and time-to-death using AFT models under Bayesian settings. They analyzed two of the data sets with various models and found out interesting results on how covariates and shared frailty affect survival outcome of the patients.…”

Section: Introductionmentioning

confidence: 99%

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Prediction of Survival of HIV/AIDS Patients from Various Sources of Data Using AFT Models

Erango¹

2017

SJAMS

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Abstract:The aim of this paper is to predict and compare the survival of HIV/AIDS patients under ART follow-up in three different hospitals in Ethiopia. Three data sets with total 1304 patients were considered. Three parametric accelerated failure time distributions: lognormal, loglogistic and Weibull are used to analyze, predict and compare survival probabilities of the patients. The results indicate that the empirical hazard rates of the three data sets reveal maximal peaks. The patients from Arba Minch hospital seems to have highest event intensity. The AFT loglogistic model is selected to best fit to each of the data sets. Different covariates except TB infection status are found to affect patients' survival at each of the hospitals. Patients with TB infection at baseline tend to have shorter survival time as compare to one with no TB infection, with significant differences of survive time between the two groups. Patients under follow-up at Shashemene hospital tend have consistently highest survival probabilities in both TB positive and negative groups. Patients from Bale Robe hospital tend to have longest survival time, while those from Arba Minch hospital have shortest survival time. Patients with bedridden status have the shortest survival time. The AFT-loglogistic is recommended in modelling time-to-event data considered in this study. The results are unique to each hospital implying that patients' care and intervention needs to be specific.

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Joint modeling of longitudinal CD4 count data and time to first occurrence of composite outcome

Iddrisu,

Azomyan

et al. 2024

Journal of Clinical Tuberculosis and Other Mycobacterial Diseas

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Bayesian Joint Modelling of Survival of HIV/AIDS Patients Using Accelerated Failure Time Data and Longitudinal CD4 Cell Counts

Cited by 15 publications

References 0 publications

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

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

Prediction of Survival of HIV/AIDS Patients from Various Sources of Data Using AFT Models

Joint modeling of longitudinal CD4 count data and time to first occurrence of composite outcome

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