Comparison of Cox Regression and Parametric Models: Application for Assessment of Survival of Pediatric Cases of Acute Leukemia in Southern Iran

Background: Finding the most appropriate regression model for survival data in cancer casesin order to determine prognosis is an important issue in medical research. Here we compare Cox and parametric regression models regarding survival of children with acute leukemia in southern Iran. Methods: In a retrospective cohort study, information for 197 children with acute leukemia over 6 years was collected through observation and interviews. In order to identify factors affecting their survival, the Cox and parametric (exponential, Weibull, log-logistic, log-normal, Gompertz and generalized gamma) models were fitted to the data. To find the best predictor model, the Akaike’s information criterion (AIC) and the Coxsnell residual were employed. Results: Out of 197 children, 164 (83.3%) had ALL and 33 (16.7%) AML; the mean (± standard deviation) survival time was 52.1±8.10 months. According to both the AIC and the Coxsnell residual, the Cox regression model was the weakest and the log-normal and Weibull models were the best for fitting to data. Based on the log-normal model, age (HR=1.01, p=0.004), residence area (HR=1.60, p=0.038) and WBC (White Blood Cell) (HR=1.57, p=0.014) had significant effects on patient survival. Conclusion: Parametric regression models demonstrate better performance as compared to the Cox model for identifying risk factors for prognosis with acute leukemia data. Just because the assumption of PH (Proportional Hazards) is held for the Cox regression model, we should not ignore parameter models.