alexa Posterior Inference for White Hispanic Breast Cancer Survival D ata | OMICS International | Abstract
ISSN: 2155-6180

Journal of Biometrics & Biostatistics
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Research Article

Posterior Inference for White Hispanic Breast Cancer Survival D ata

Hafiz MR Khan1*, Anshul Saxena2 and Alice Shrestha1

1Department of Biostatistics, Robert Stempel College of Public Health & Social Work Florida International University, Miami, FL 33199, USA

2Department of Health Promotion & Disease Prevention, Robert Stempel College of Public Health & Social Work, Florida International University, Miami, FL 33199, USA

*Corresponding Author:
Hafiz MR Khan
Department of Biostatistics
Robert Stempel College of Public Health & Social Work Florida International University
Miami, FL 33199, USA
Tel: +001-305-348-4908
Fax: +001-305-348-4901
E-mail: [email protected]

Received date: November 18, 2013; Accepted date: January 11, 2014; Published date: January 18, 2014

Citation: Khan HMR, Saxena A, Shrestha A (2014) Posterior Inference for White Hispanic Breast Cancer Survival Data. J Biomet Biostat 5: 183. doi: 10.4172/2155-6180.1000183

Copyright: © 2014 Khan HMR, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are are credited.

Abstract

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.

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