ArticleRisk management and healthcare policy2020
Bayesian Joint Modeling of Longitudinal and Survival Time Measurement of Hypertension Patients.
Article in Risk management and healthcare policy, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed, 3 citations in OpenAlex.
- Joint modeling of longitudinal change of fasting blood sugar and systolic blood pressure with survival time to death among hypertension patients.Scientific reports · 2023Article
- Joint modeling of blood pressure measurement and survival time of hypertension patients.Scientific reports · 2021Article
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1 author at 1 institution in 1 country.
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Abstract
backgroundHigh 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.
objectiveThis 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.
methodsWe 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.
resultsThe 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.
conclusionThe 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.
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