title: Statistical applications for the prediction of white Hispanic breast cancer survival.
abstract: BACKGROUND
  The ability to predict the survival time of breast cancer patients is important because of the potential high morbidity and mortality associated with the disease. To develop a predictive inference for determining the survival of breast cancer patients, we applied a novel Bayesian method. In this paper, we propose the development of a databased statistical probability model and application of the Bayesian method to predict future survival times for White Hispanic female breast cancer patients, diagnosed in the US during 1973-2009.
  
  
  METHODS
  A stratified random sample of White Hispanic female patient survival data was selected from the Surveillance Epidemiology and End Results (SEER) database to derive statistical probability models. Four were considered to identify the best-fit model. We used three standard model-building criteria, which included Akaike Information Criteria (AIC), Bayesian Information Criteria (BIC), and Deviance Information Criteria (DIC) to measure the goodness of fit. Furthermore, the Bayesian method was used to derive future survival inferences for survival times.
  
  
  RESULTS
  The highest number of White Hispanic female breast cancer patients in this sample was from New Mexico and the lowest from Hawaii. The mean (SD) age at diagnosis (years) was 58.2 (14.2). The mean (SD) of survival time (months) for White Hispanic females was 72.7 (32.2). We found that the exponentiated Weibull model best fit the survival times compared to other widely known statistical probability models. The predictive inference for future survival times is presented using the Bayesian method.
  
  
  CONCLUSIONS
  The findings are significant for treatment planning and health-care cost allocation. They should also contribute to further research on breast cancer survival issues.
authors: Department of Biostatistics, Robert Stempel College of Public Health and Social Work, Florida International University, Miami, USA E-mail : hmkhan@fiu.edu.
authors: Hafiz Mohammad Rafiqullah
authors: HM
authors: Khan
authors: Anshul
authors: A
authors: Saxena
authors: Kemesha
authors: K
authors: Gabbidon
authors: Elizabeth
authors: E
authors: Ross
authors: Alice
authors: A
authors: Shrestha
mesh_terms: D001499:Bayes Theorem; D001943:Breast Neoplasms / Q000401:mortality*; D005260:Female; D006630:Hispanic or Latino; D006801:Humans; D008875:Middle Aged; D015233:Models, Statistical; D011379:Prognosis; D018426:SEER Program; D015996:Survival Rate; D014481:United States