Estimation Of Cure Rate In Iranian Breast Cancer Patients

Baghestani AHMADREZA, Shahid Beheshti University of Medical Sciences, Iran

1 Department of Biostatistics, Faculty of Paramedical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
2 Alborz University of Medical Sciences, Karaj, Iran
3 Gastroenterology and Liver Diseases Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran
4 Hospital management Research Center, Iran University of Medical Sciences, Theran, Iran

Purpose: Although the Cox’s proportional hazard model is the popular survival analysis to investigate the significant risk factors of cancer patient's survival, this model would not appropriate in the case of log-term disease free survival. Recently, cure rate models, introduced to distinguish between clinical determinants of cure and variables associated with the time to event of interest. The aim of this study was to use these cure rate model to determine the clinical associated factors on cure rate of patients with breast cancer (BC).
Methods: This is a prospective cohort study on 305 patients with breast cancer, admitted at Shahid Faiazbakhsh Hospital, Tehran, during 2006 to 2008 and followed until April 2012. The case of patients’ death was confirmed by telephone contact. For data analysis, non-mixed cure rate model with Poisson distribution and Negative Binomial distribution were employed. All analysis carried out using a developed Macro in WinBugs. The DIC criteria employed to find the best model. 
Results: The overall 1-year, 3-year and 5-year relative survival rate was found as 97%, 89% and 74%. Metastasis and stage of BC were the significant factors, but age was significant only in Negative Binomial model. The DIC criteria showed that the Negative Binomial model had a better fit.
Conclusion: This study indicated that, metastasis and stage of BC were identified as the clinical criteria of cure rate. There are limited studies on BC survival, which employed these cure rate models to identify the clinical factors associated with cure. These models are better than Cox, in the case of long-term survival, even thought there are no cured patients.