Bayesian Joinpoint Regression Model for Childhood Brain Cancer Mortality

Authors

Ram C. Kafle, University of South Florida, Tampa, FLFollow
Netra Khanal, The University of Tampa, Tampa, FLFollow
Chris P. Tsokos, University of South Florida, Tampa, FLFollow

Abstract

The Bayesian approach of joinpoint regression is widely used to analyze trends in cancer mortality, incidence and survival data. The Bayesian joinpoint regression model was used to study the childhood brain cancer mortality rate and its average percentage change (APC) per year. Annual observed mortality counts of children ages 0-19 from 1969-2009 obtained from Surveillance Epidemiology and End Results (SEER) database of National Cancer Institute (NCI) were analyzed. It was assumed that death counts are probabilistically characterized by the Poisson distribution and they were modeled using log link function. Results were compared with the mortality trend obtained using joinpoint software of NCI.

DOI

10.22237/jmasm/1383279660

Erratum

In the original running head of this article, we incorrectly spelled author Netra Khanal's surname. We regret this error, and have corrected the article.