Statistical Estimates from Black Non-Hispanic Female Breast Cancer Data


Background: The use of statistical methods has become an imperative tool in breast cancer survival dataanalysis. The purpose of this study was to develop the best statistical probability model using the Bayesian methodto predict future survival times for the black non-Hispanic female breast cancer patients diagnosed during 1973-2009 in the U.S. Materials and
Methods: We used a stratified random sample of black non-Hispanic female breastcancer patient data from the Surveillance Epidemiology and End Results (SEER) database. Survival analysiswas performed using Kaplan-Meier and Cox proportional regression methods. Four advanced types of statisticalmodels, Exponentiated Exponential (EE), Beta Generalized Exponential (BGE), Exponentiated Weibull (EW),and Beta Inverse Weibull (BIW) were utilized for data analysis. The statistical model building criteria, AkaikeInformation Criteria (AIC), Bayesian Information Criteria (BIC), and Deviance Information Criteria (DIC) wereused to measure the goodness of fit tests. Furthermore, we used the Bayesian approach to obtain the predictivesurvival inferences from the best-fit data based on the exponentiated Weibull model.
Results: We identified thehighest number of black non-Hispanic female breast cancer patients in Michigan and the lowest in Hawaii. Themean (SD), of age at diagnosis (years) was 58.3 (14.43). The mean (SD), of survival time (months) for black non-Hispanic females was 66.8 (30.20). Non-Hispanic blacks had a significantly increased risk of death comparedto Black Hispanics (Hazard ratio: 1.96, 95%CI: 1.51–2.54). Compared to other statistical probability models,we found that the exponentiated Weibull model better fits for the survival times. By making use of the Bayesianmethod predictive inferences for future survival times were obtained.
Conclusions: These findings will be ofgreat significance in determining appropriate treatment plans and health-care cost allocation. Furthermore, thesame approach should contribute to build future predictive models for any health related diseases.