title: Survival Analysis of Patients with Breast Cancer using Weibull Parametric Model.
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.
  
  
  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.
authors.affiliation: Department of Biostatistics, Faculty of Paramedical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran E-mail : s.saeedimoghaddam@gmail.com.
authors.forename: Ahmad Reza
authors.initials: AR
authors.lastname: Baghestani
authors.forename: Sahar Saeedi
authors.initials: SS
authors.lastname: Moghaddam
authors.forename: Hamid Alavi
authors.initials: HA
authors.lastname: Majd
authors.forename: Mohammad Esmaeil
authors.initials: ME
authors.lastname: Akbari
authors.forename: Nahid
authors.initials: N
authors.lastname: Nafissi
authors.forename: Kimiya
authors.initials: K
authors.lastname: Gohari
mesh_terms: D000328:Adult; D000367:Age Factors; D000368:Aged; D001943:Breast Neoplasms / Q000378:metabolism / Q000401:mortality* / Q000473:pathology / Q000628:therapy; D003131:Combined Modality Therapy; D005260:Female; D005500:Follow-Up Studies; D006801:Humans; D008198:Lymph Nodes / Q000473:pathology*; D008875:Middle Aged; D015233:Models, Statistical; D015999:Multivariate Analysis; D060787:Neoplasm Grading; D009361:Neoplasm Invasiveness; D009367:Neoplasm Staging; D011379:Prognosis; D011960:Receptors, Estrogen / Q000378:metabolism; D011980:Receptors, Progesterone / Q000378:metabolism; D015996:Survival Rate
chemical_list: D011960:Receptors, Estrogen; D011980:Receptors, Progesterone