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Survival Analysis of Patients with Breast Cancer using Weibull Parametric Model

  • 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 : 2016.01.11

Abstract

Background: The Cox model is known as one of the most frequently-used methods for analyzing survival data. However, in some situations parametric methods may provide better estimates. In this study, a Weibull parametric model was employed to assess possible prognostic factors that may affect the survival of patients with breast cancer. Materials and Methods: We studied 438 patients with breast cancer who visited and were treated at the Cancer Research Center in Shahid Beheshti University of Medical Sciences during 1992 to 2012; the patients were followed up until October 2014. Patients or family members were contacted via telephone calls to confirm whether they were still alive. Clinical, pathological, and biological variables as potential prognostic factors were entered in univariate and multivariate analyses. The log-rank test and the Weibull parametric model with a forward approach, respectively, were used for univariate and multivariate analyses. All analyses were performed using STATA version 11. A P-value lower than 0.05 was defined as significant. Results: On univariate analysis, age at diagnosis, level of education, type of surgery, lymph node status, tumor size, stage, histologic grade, estrogen receptor, progesterone receptor, and lymphovascular invasion had a statistically significant effect on survival time. On multivariate analysis, lymph node status, stage, histologic grade, and lymphovascular invasion were statistically significant. The one-year overall survival rate was 98%. Conclusions: Based on these data and using Weibull parametric model with a forward approach, we found out that patients with lymphovascular invasion were at 2.13 times greater risk of death due to breast cancer.

Keywords

Breast cancer;survival analysis;Weibull parametric model;log-rank test

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