Background: Race and ethnicity are significant factors in predicting survival time of breast cancer patients. Inthis study, we applied advanced statistical methods to predict the survival of White non-Hispanic female breastcancer patients, who were diagnosed between the years 1973 and 2009 in the United States (U.S.). Materialsand Methods: Demographic data from the Surveillance Epidemiology and End Results (SEER) database wereused for the purpose of this study. Nine states were randomly selected from 12 U.S. cancer registries. A stratifiedrandom sampling method was used to select 2,000 female breast cancer patients from these nine states. Wecompared four types of advanced statistical probability models to identify the best-fit model for the White non-Hispanic female breast cancer survival data. Three model building criterion were used to measure and comparegoodness of fit of the models. These include Akaike Information Criteria (AIC), Bayesian Information Criteria(BIC), and Deviance Information Criteria (DIC). In addition, we used a novel Bayesian method and the MarkovChain Monte Carlo technique to determine the posterior density function of the parameters. After evaluatingthe model parameters, we selected the model having the lowest DIC value. Using this Bayesian method, wederived the predictive survival density for future survival time and its related inferences. Results: The analyticalsample of White non-Hispanic women included 2,000 breast cancer cases from the SEER database (1973-2009).The majority of cases were married (55.2%), the mean age of diagnosis was 63.61 years (SD = 14.24) and themean survival time was 84 months (SD = 35.01). After comparing the four statistical models, results suggestedthat the exponentiated Weibull model (DIC= 19818.220) was a better fit for White non-Hispanic females’ breastcancer survival data. This model predicted the survival times (in months) for White non-Hispanic women afterimplementation of precise estimates of the model parameters. Conclusions: By using modern model buildingcriteria, we determined that the data best fit the exponentiated Weibull model. We incorporated precise estimatesof the parameter into the predictive model and evaluated the survival inference for the White non-Hispanicfemale population. This method of analysis will assist researchers in making scientific and clinical conclusionswhen assessing survival time of breast cancer patients.
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