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Application of a Non-Mixture Cure Rate Model for Analyzing Survival of Patients with Breast Cancer

  • Baghestani, Ahmad Reza (Department of Biostatistics, Faculty of Paramedical Sciences, Shahid Beheshti University of Medical Sciences) ;
  • Moghaddam, Sahar Saeedi (Department of Biostatistics, Faculty of Paramedical Sciences, Shahid Beheshti University of Medical Sciences) ;
  • Majd, Hamid Alavi (Department of Biostatistics, Faculty of Paramedical Sciences, Shahid Beheshti University of Medical Sciences) ;
  • Akbari, Mohammad Esmaeil (Cancer Research Center, Shahid Beheshti University of Medical Sciences) ;
  • Nafissi, Nahid (Cancer Research Center, Shahid Beheshti University of Medical Sciences) ;
  • Gohari, Kimiya (Department of Biostatistics, Faculty of Paramedical Sciences, Shahid Beheshti University of Medical Sciences)
  • Published : 2015.11.04

Abstract

Background: As a result of significant progress made in treatment of many types of cancers during the last few decades, there have been an increased number of patients who do not experience mortality. We refer to these observations as cure or immune and models for survival data which include cure fraction are known as cure rate models or long-term survival models. Materials and Methods: In this study we used the data collected from 438 female patients with breast cancer registered in the Cancer Research Center in Shahid Beheshti University of Medical Sciences, Tehran, Iran. The patients had been diagnosed from 1992 to 2012 and were followed up until October 2014. We had to exclude some because of incomplete information. Phone calls were made to confirm whether the patients were still alive or not. Deaths due to breast cancer were regarded as failure. To identify clinical, pathological, and biological characteristics of patients that might have had an effect on survival of the patients we used a non-mixture cure rate model; in addition, a Weibull distribution was proposed for the survival time. Analyses were performed using STATA version 14. The significance level was set at $P{\leq}0.05$. Results: A total of 75 patients (17.1%) died due to breast cancer during the study, up to the last follow-up. Numbers of metastatic lymph nodes and histologic grade were significant factors. The cure fraction was estimated to be 58%. Conclusions: When a cure fraction is not available, the analysis will be changed to standard approaches of survival analysis; however when the data indicate that the cure fraction is available, we suggest analysis of survival data via cure models.

Keywords

Breast cancer;long-term survival analysis;non-mixture cure rate model

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