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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

DOI https://doi.org/10.2147/RMHP.S222425

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 markos.erango73@gmail.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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