Bayesian Method for Modeling Male Breast Cancer Survival Data


Background: With recent progress in health science administration, a huge amount of data has been collectedfrom thousands of subjects. Statistical and computational techniques are very necessary to understand suchdata and to make valid scientific conclusions. The purpose of this paper was to develop a statistical probabilitymodel and to predict future survival times for male breast cancer patients who were diagnosed in the USA during1973-2009. Materials and
Methods: A random sample of 500 male patients was selected from the SurveillanceEpidemiology and End Results (SEER) database. The survival times for the male patients were used to derivethe statistical probability model. To measure the goodness of fit tests, the model building criterions: AkaikeInformation 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 parametersand the predictive inference for future survival times from the exponentiated Weibull model, assuming that theobserved breast cancer survival data follow such type of model. The Markov chain Monte Carlo method wasused to determine the inference for the parameters.
Results: The summary results of certain demographic andsocio-economic variables are reported. It was found that the exponentiated Weibull model fits the male survivaldata. 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 hopefullybe useful in treatment planning, healthcare resource allocation, and may motivate future research on breastcancer related survival issues.