title: Statistical estimates from black non-Hispanic female breast cancer data.
abstract: BACKGROUND
  The use of statistical methods has become an imperative tool in breast cancer survival data analysis. The purpose of this study was to develop the best statistical probability model using the Bayesian method to predict future survival times for the black non-Hispanic female breast cancer patients diagnosed during 1973- 2009 in the U.S.
  
  
  METHODS
  We used a stratified random sample of black non-Hispanic female breast cancer patient data from the Surveillance Epidemiology and End RESULTS (SEER) database. Survival analysis was performed using Kaplan-Meier and Cox proportional regression methods. Four advanced types of statistical models, Exponentiated Exponential (EE), Beta Generalized Exponential (BGE), Exponentiated Weibull (EW), and Beta Inverse Weibull (BIW) were utilized for data analysis. The statistical model building criteria, Akaike Information Criteria (AIC), Bayesian Information Criteria (BIC), and Deviance Information Criteria (DIC) were used to measure the goodness of fit tests. Furthermore, we used the Bayesian approach to obtain the predictive survival inferences from the best-fit data based on the exponentiated Weibull model.
  
  
  RESULTS
  We identified the highest number of black non-Hispanic female breast cancer patients in Michigan and the lowest in Hawaii. The mean (SD), of age at diagnosis (years) was 58.3 (14.43). The mean (SD), of survival time (months) for black non- Hispanic females was 66.8 (30.20). Non-Hispanic blacks had a significantly increased risk of death compared to Black Hispanics (Hazard ratio: 1.96, 95%CI: 1.51-2.54). Compared to other statistical probability models, we found that the exponentiated Weibull model better fits for the survival times. By making use of the Bayesian method predictive inferences for future survival times were obtained.
  
  
  CONCLUSIONS
  These findings will be of great significance in determining appropriate treatment plans and health-care cost allocation. Furthermore, the same approach should contribute to build future predictive models for any health related diseases.
authors.affiliation: Department of Biostatistics, Robert Stempel College of Public Health and Social Work, Florida International University, Miami, USA E-mail : hmkhan@fiu.edu.
authors.forename: Hafiz Mohammad Rafiqullah
authors.initials: HM
authors.lastname: Khan
authors.forename: Boubakari
authors.initials: B
authors.lastname: Ibrahimou
authors.forename: Anshul
authors.initials: A
authors.lastname: Saxena
authors.forename: Kemesha
authors.initials: K
authors.lastname: Gabbidon
authors.forename: Faheema
authors.initials: F
authors.lastname: Abdool-Ghany
authors.forename: Venkataraghavan
authors.initials: V
authors.lastname: Ramamoorthy
authors.forename: Duff
authors.initials: D
authors.lastname: Ullah
authors.forename: Tiffanie Shauna-Jeanne
authors.initials: TS
authors.lastname: Stewart
mesh_terms: D001499:Bayes Theorem; D044383:Black People / Q000706:statistics & numerical data*; D001943:Breast Neoplasms / Q000401:mortality*; D005006:Ethnicity / Q000706:statistics & numerical data*; D005260:Female; D005500:Follow-Up Studies; D006801:Humans; D008875:Middle Aged; D015233:Models, Statistical; D011379:Prognosis; D018426:SEER Program; D015996:Survival Rate