Journal of Biometrics & Biostatistics

Posterior Inference for White Hispanic Breast Cancer Survival D ata

Abstract

Hafiz MR Khan, Anshul Saxena and Alice Shrestha

The purpose of this paper is to develop a statistical probability model and to obtain posterior inference for the parameters given the survival times of the White Hispanic female cancer patients. Stratified random sample of White Hispanic female patients’ survival data was used to derive a best fit statistical probability model. The study sample was extracted from the Surveillance Epidemiology and End Results (SEER) cancer registry database. Three model building criterions were utilized; Akaike Information Criteria (AIC), Bayesian Information Criteria (BIC), and Deviance Information Criteria (DIC) to measure the goodness of fit. We found that the Exponentiated Weibull model fits the survival times better as compared to other widely known statistical probability models. The Bayesian approach is employed to derive the posterior inference for the parameters.