Bayesian Joint Modeling of Longitudinal and Survival Time Measurement of Hypertension Patients
Authors Erango MA
Received 9 July 2019
Accepted for publication 14 January 2020
Published 4 February 2020 Volume 2020:13 Pages 73—81
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 3
Editor who approved publication: Dr Kent Rondeau
Markos Abiso Erango
College of Natural Science, Department of Statistics, Arba Minch University, Arba Minch, Ethiopia
Correspondence: Markos Abiso Erango Email email@example.com
Background: High blood pressure is a health risk for all populations, worldwide. Globally the number of people with uncontrolled hypertension rose by 70% between 1980 and 2008.
Objective: This paper aims to investigate the association of survival time and fasting blood sugar levels of hypertension patients and identify the risk factors that affect the survival time of the patient.
Methods: We considered a total of 430 random samples of hypertension patients who were followed-up at Yekatit-12 Hospital in Ethiopia from January 2013 to January 2019. A linear mixed effects model was used for the longitudinal outcomes (fasting blood sugar) with normality assumption, although four parametric accelerated failure time distributions: exponential, Weibull, lognormal and loglogistic are studied for the time-to-event data. The Bayesian joint models were defined through latent variables and association parameters and with specified noninformative prior distributions for the model parameters. Simulations are conducted using Gibbs sampler algorithm implemented in the WinBUGS software. The model selection criteria DIC is employed to identify the model with best fit to the data.
Results: The findings from Bayesian joint models are consistent. The association parameter in each Bayesian joint model is significant. This implies that there is dependence between the two processes: longitudinal fasting blood sugar level and the time-to-death event under joint models. With investigation of the model comparison criteria, the Bayesian–Weibull model was preferred to analysize the current data sets. Based on joint analysis the baseline age, place of residence, family history of hypertension, khat intake, blood cholesterol level of the patient, hypertension disease stage, adherence to the treatment and related disease were associated factors that affect the survival time of hypertension patients.
Conclusion: The analysis suggests that there is strong association between longitudinal process (fasting blood sugar) and time-to-event data. The researcher recommends that all stakeholders should be aware of the consequences of these factors which can influence the survival time of hypertension patients in the study area.
Keywords: Bayesian, joint model, hypertension, survival analysis, parametric models
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