title: Fitting Cure Rate Model to Breast Cancer Data of Cancer Research Center.
abstract: 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 may not be the most appropriate. In such cases, a cure rate model seems more suitable. The purpose of this study was to determine clinical factors associated with cure rate of patients with breast cancer.
  
  
  METHODS
  In order to find factors affecting cure rate (response), a non-mixed cure rate model with negative binomial distribution for latent variable was used. Variables selected were recurrence cancer, status for HER2, estrogen receptor (ER) and progesterone receptor (PR), size of tumor, grade of cancer, stage of cancer, type of surgery, age at the diagnosis time and number of removed positive lymph nodes. All analyses were performed using PROC MCMC processes in the SAS 9.2 program.
  
  
  RESULTS
  The mean (SD) age of patients was equal to 48.9 (11.1) months. For these patients, 1, 5 and 10-year survival rates were 95, 79 and 50 percent respectively. All of the mentioned variables were effective in cure fraction. Kaplan-Meier curve showed cure model's use competence.
  
  
  CONCLUSIONS
  Unlike other variables, existence of ER and PR positivity will increase probability of cure in patients. In the present study, Weibull distribution was used for the purpose of analysing survival times. Model fitness with other distributions such as log-N and log-logistic and other distributions for latent variable is recommended.
authors.affiliation: Researcher, Cancer Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran E-mail : pshkhoei@gmail.com.
authors.forename: Ahmad Reza
authors.initials: AR
authors.lastname: Baghestani
authors.forename: Farid
authors.initials: F
authors.lastname: Zayeri
authors.forename: Mohammad Esmaeil
authors.initials: ME
authors.lastname: Akbari
authors.forename: Leyla
authors.initials: L
authors.lastname: Shojaee
authors.forename: Naghmeh
authors.initials: N
authors.lastname: Khadembashi
authors.forename: Parviz
authors.initials: P
authors.lastname: Shahmirzalou
mesh_terms: D000328:Adult; D001943:Breast Neoplasms / Q000401:mortality* / Q000628:therapy*; D015331:Cohort Studies; D005260:Female; D006801:Humans; D053208:Kaplan-Meier Estimate; D008198:Lymph Nodes / Q000601:surgery; D008207:Lymphatic Metastasis / Q000473:pathology; D008875:Middle Aged; D060787:Neoplasm Grading; D009364:Neoplasm Recurrence, Local / Q000628:therapy*; D016016:Proportional Hazards Models; D011446:Prospective Studies; D018719:Erb-b2 Receptor Tyrosine Kinases / Q000378:metabolism; D011960:Receptors, Estrogen / Q000378:metabolism*; D011980:Receptors, Progesterone / Q000378:metabolism*; D015996:Survival Rate; D016896:Treatment Outcome
chemical_list: D018719:Erb-b2 Receptor Tyrosine Kinases; D011960:Receptors, Estrogen; D011980:Receptors, Progesterone; C508053:ERBB2 protein, human