Bayesian Approach to Age-Adjusted Joinpoint Regression Model

• Activity Number: 38
• Type: Contributed
• Date/Time: Sunday, August 4, 2013 : 2:00 PM to 3:50 PM
• Sponsor: Section on Bayesian Statistical Science
• Author(s): Ram C. Kafle*+ and Netra Khanal and Chris Tsokos
• Companies: University of South Florida and University of Tampa and University of South Florida
• Keywords: Bayesian ; Joinpoint ; SEER ; Mortality ; Incidence

Abstract

Joinpoint regression model identifies significant changes in the trends of the incidence, mortality, and survival of a specific disease in a given population. The purpose of the present study is to develop an age-adjusted Bayesian joinpoint regression model to describe mortality or incidence trend assuming that the observed counts are probabilistically characterized by the Poisson distribution. The proposed model is based on Bayesian model selection criteria with the smallest number of joinpoints that are sufficient to explain the Annual Percentage Change(APC). The prior probability distributions are chosen in such a way that they are automatically derived from the model index contained in the model space. In developing the subject methods, we use the cancer mortality counts of adult brain and other Central Nervous System (CNS) cancer patients obtained from the Surveillance Epidemiology and End Results (SEER) data base of National Cancer Institute.