title: Bayesian method for modeling male breast cancer survival data.
abstract: BACKGROUND
  With recent progress in health science administration, a huge amount of data has been collected from thousands of subjects. Statistical and computational techniques are very necessary to understand such data and to make valid scientific conclusions. The purpose of this paper was to develop a statistical probability model and to predict future survival times for male breast cancer patients who were diagnosed in the USA during 1973-2009.
  
  
  METHODS
  A random sample of 500 male patients was selected from the Surveillance Epidemiology and End Results (SEER) database. The survival times for the male patients were used to derive the statistical probability model. To measure the goodness of fit tests, the model building criterions: Akaike Information Criteria (AIC), Bayesian Information Criteria (BIC), and Deviance Information Criteria (DIC) were employed. A novel Bayesian method was used to derive the posterior density function for the parameters and the predictive inference for future survival times from the exponentiated Weibull model, assuming that the observed breast cancer survival data follow such type of model. The Markov chain Monte Carlo method was used to determine the inference for the parameters.
  
  
  RESULTS
  The summary results of certain demographic and socio-economic variables are reported. It was found that the exponentiated Weibull model fits the male survival data. Statistical inferences of the posterior parameters are presented. Mean predictive survival times, 95% predictive intervals, predictive skewness and kurtosis were obtained.
  
  
  CONCLUSIONS
  The findings will hopefully be useful in treatment planning, healthcare resource allocation, and may motivate future research on breast cancer related survival issues.
authors.affiliation: Department of Biostatistics, Robert Stempel College of Public Health and Social Work, Florida International University, Florida, USA E-mail : hmkhan@fiu.edu.
authors.forename: Hafiz Mohammad Rafiqullah
authors.initials: HM
authors.lastname: Khan
authors.forename: Anshul
authors.initials: A
authors.lastname: Saxena
authors.forename: Sagar
authors.initials: S
authors.lastname: Rana
authors.forename: Nasar Uddin
authors.initials: NU
authors.lastname: Ahmed
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D001499:Bayes Theorem; D018567:Breast Neoplasms, Male / Q000401:mortality*; D005500:Follow-Up Studies; D006801:Humans; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D009367:Neoplasm Staging; D011379:Prognosis; D018426:SEER Program; D015996:Survival Rate