Survival Time Analysis of Hypertension Patients Using Parametric Models

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Markos Abiso Erango
Kabtamu Tolosie Gergiso
Sultan Hussen Hebo


Background: Hypertension is a worldwide public-health challenge and one of a leading modifiable risk factor for cardiovascular disease and death.

Aims: The aim of this study was compare parameter estimations using both Bayesian and classical approaches and to detect out potential factors that affects survival probability of hypertension patient’s under follow up.

Materials and Methods: A simple random sampling technique was used to select 430 patients among a total of 2126 hypertension patients who had been under follow up at Yekatit-12 Hospital in Ethiopia from January 2013 to January 2019. Parametric distributions: Exponential, Weibull, Lognormal and loglogistic are studied to analysis survival probabilities of the patients in both Bayesian and classical approaches. The model selection criteria are employed to identify the model with best fit to the data. Bayesian estimation approach was smaller deviance information criteria as compare to classical estimation approaches for the current data set.

Results and Conclusion: The analysis Bayesian Weibull results indicate that the baseline age of the patient, gender, family history of hypertension, tobacco use, alcohol use, khat intake, blood cholesterol level of the patient, hypertension disease stage, adherence to the treatment and related disease were significantly associated with survival time of hypertension patients. Patients with raised blood Cholesterol level at baseline tend to have shorter survival time as compare to one with normal blood cholesterol level at baseline. Society and all stakeholders should be aware of the consequences of these factors which can influence the survival time of hypertension patients.

Bayesian approach, classical approaches, estimation methods, hypertension, survival probability.

Article Details

How to Cite
Erango, M. A., Gergiso, K. T., & Hebo, S. H. (2019). Survival Time Analysis of Hypertension Patients Using Parametric Models. Advances in Research, 20(2), 1-10.
Original Research Article


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