Fitting cure rate model to breast cancer data of Cancer Research Center

Background: The Cox PH model is one of the most significant statistical models in studying survival of patients. But in the case of patients with long-term survival, it is not to be a proper one model. In such cases, cure rate model seems a more suitable. The purpose of this study is to determine clinical factors associated with cure rate of patients with breast cancer. Materials and Methods: Cancer Research Center has started identifying and recording the characteristics of the patients with breast cancer from 1994 to 2013, leading to a follow up in the form of a record of the given event . In order to find factors affecting cure rate (response), a Non-Mixed cure rate modelwith Negative Binomial distribution was used. All analyses were performed using PROC MCMC process in SAS 9.2 program. Results: The mean (SD) age of patients was equal to 48.86 (11.13) months. For these patients, 1, 5 and 10-years of survival were 95, 79 and 50% respectively. All of the mentioned variables were effective in cure fraction. Kaplan-Meier curve has proven the existence of cured patients and showed cure model’s use competence. Conclusion: This study exhibited which variables were clinical measure in patient’s cure rate. These models proved superior to Cox, in the case of long-term survival. These cases are needed to be addressed and expanded more. In the present study, Weibull distribution was used for the purpose of survival times. Model fitness with other distributions such as log-N and loglogistic and other distributions for hidden variable is recommended.

Keywords:

Breat Neoplasms , Cure Fraction , Cure Rate Model , Iran , Negative Binomial Distribution , Survival Analysis