Comparison of Parametric Models: Appication to Hypertensive Patients in a Teaching Hospital, Awka

Abstract

Introduction: In Nigeria, hypertension is a common sickness among grownups. This research was carried out to determine the best model for predicting survival of hypertensive patients using goodness of fit criteria, Standard Error (SE), Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). Method: A total of 105 patients who were diagnosed with hypertension from January 2013 to July 2018 were considered in which death is the event of interest. Six parametric models such as; exponential, Weibull, Lognormal, Log-logistic, Gompertz and hypertabastic distribution were fitted to the data using goodness of fit such as S.E, AIC and BIC to determine the best model. The parametric models were considered because they are all lifetime distributions. Results The result shows that the hypertabastic distribution has the lowest AIC and BIC, followed by Gompertz distribution. The standard error also indicates the hypertabastic model is better because it has the least value of standard error. This indicates that in terms of relative efficiency and parameterization the hypertabastic model is the best. The Survival Probability Plot of the six parametric models shows that the Hypertabastic distribution best fitted the data because it shows a clear step function than the other distribution and this justifies the result SE, AIC and BIC presented. Conclusion: Since hypertabastic distribution has the lowest SE, AIC and BIC it indicates that it is the best parametric model for predicting survival of hypertensive patients in chukwuemeka Odumegwu Ojukwu university teaching hospital Awka, Nigeria.