Survival Time Analysis of Hypertension Patients Using Parametric Models

Abstract

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 Erango et al.; AIR, 20(2): 1-10, 2019; Article no.AIR.52009 2 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. Original Research Article

A principled distance-based prior for the shape of the Weibull model

“…Since then, various priors (both objective and subjective) for the shape parameter have been 15 proposed as options in Bayesian analysis. The improper uniform and gamma priors have been used in various studies for the shape of the Weibull model, see for example Erango et al (2019), also the joint Jeffrey's prior (Guure and Ibrahim, 2012) or other uninformative priors (Sun, 1997). Most works use the shape parameter itself as a departure point, by assigning some known density subjective/objective prior knowledge, and posit that a prior should be formed for a property of the model instead of a parameter.…”

A principled distance-based prior for the shape of the Weibull model

“…as opposed to the constant hazard function from the exponential model. The Weibull model is still popular in many applied sciences, including reliability and survival analysis [5,12,7,10,23], wind speed modeling [28,3,14] and quality engineering [31,11], to mention a few. However, this flexibility should also propel the cautious estimation of the shape parameter.…”

“…Improper or noninformative priors have been popular for some time, but the need for weakly informative priors has been realised [21]. The improper uniform and the gamma priors have been used in various studies for the shape of the Weibull model, see [2], [12], [5], [23], [9] and [16] amongst others. A joint Jeffrey's prior proposed by [17,16] is also a popular choice amongst practitioners [4,16].…”