Abstract #300127

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JSM 2003 Abstract #300127
Activity Number: 320
Type: Topic Contributed
Date/Time: Wednesday, August 6, 2003 : 8:30 AM to 10:20 AM
Sponsor: Section on Bayesian Stat. Sciences
Abstract - #300127
Title: A Bayesian Model Selection Approach for Joinpoint Regression with Application to Age-Adjusted Cancer Rates
Author(s): Ram C. Tiwari*+
Companies: National Institutes of Health
Address: 6116 Executive Blvd., MSC 8317, Suite 5039, Bethesda, MD, 20892-0001,
Keywords: marginal likelihood ; permutation test ; Bayes factor
Abstract:

A Bayesian analysis of joinpoint regression model is considered and a model selection approach based on the Bayes factor is developed. Given a set of (K+1) joinpoint models, with 0,1,., K, joinpoints, the method compares any two joinpoint models through the Bayes factor that requires computation of the marginal likelihood of each competing model from the output of Markov chain Monte Carlo simulations. The Bayesian model selection of joinpoint regression is encompassed by the prior-posterior analysis of the parameters of the joinpoint model. The model corresponding to the largest Bayes factor is selected as the best model. The method is applied to analyze the observed U.S. cancer incidence rates for some selected cancer sites from the National Cancer Institute's SEER program. The graphs of the joinpoint models to the data are produced using the Bayesian method and the method that is based on the permutation test recently developed by NCI and available at the web site: http://srab.cancer.gov/joinpoint/.

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