Journal of North Khorasan

Abstract Background & Objectives: Cox proportional hazard regression model is the most common and standard method to study the effect of potential covariates on the survival times. However, under certain conditions such as availability of a good information about the system under study, parametric models are known to be more efficient than the Cox models. In this study, we aim to evaluate the efficiency of the semi-parametric Cox models and alternative parametric models and nonparametric Kaplan-Meier estimates in analyzing the survival times of patients suffering from acute myocardial infraction Material & Methods: This study is a prospective-cohort research with the total number of 650 participant's experienced acute myocardial infarction. The patients followed up for one year in order to analyze their survival times. Three approaches non-parametric, semi-parametric and parametric models, were used to model the survival times. To evaluate the relative goodness of fit of each procedures, Akaike information criterion (AIC) was used. Results: During one year of the following up, the total number of 67 deaths were reported. Out of 460 initial patients, 48% were smoking, 33.4% suffered from hypertension, 17.7% diabetes, 15.3 Hyperlipidemia, 30% airtime, and 10.3% had block. A broad agreement was observed between parametric and Cox models. Both models associated similar covariates as significant factors. However, the non-parametric model suggested high age , non used streptokinase and block significantly increased the risk of death. Conclusion: However, the hazard ratio in Cox model and parametric models were similar and results of nonparametric Kaplan Meier approach were largely similar but, The AIC suggested the parametric Weibull model as the most appropriate model for modeling the survival times of patients with acute myocardial infarction.