Statistical Applications for the Prediction of White Hispanic Breast Cancer Survival

Background: The ability to predict the survival time of breast cancer patients is important because ofthe potential high morbidity and mortality associated with the disease. To develop a predictive inference fordetermining the survival of breast cancer patients, we applied a novel Bayesian method. In this paper, we proposethe development of a databased statistical probability model and application of the Bayesian method to predictfuture survival times for White Hispanic female breast cancer patients, diagnosed in the US during 1973-2009.Materials and
Methods: A stratified random sample of White Hispanic female patient survival data was selectedfrom 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, whichincluded Akaike Information Criteria (AIC), Bayesian Information Criteria (BIC), and Deviance InformationCriteria (DIC) to measure the goodness of fit. Furthermore, the Bayesian method was used to derive future survivalinferences for survival times.
Results: The highest number of White Hispanic female breast cancer patients inthis 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 theexponentiated Weibull model best fit the survival times compared to other widely known statistical probabilitymodels. 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 contributeto further research on breast cancer survival issues.