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Activity Number: 36 - Methods for Cancer Epidemiology
Type: Contributed
Date/Time: Sunday, July 29, 2018 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistics in Epidemiology
Abstract #330151 Presentation
Title: Bayesian Joinpoint Regression Model to Study the Effect of Smoking on Lung Cancer Incidence
Author(s): Ram C. Kafle* and Melinda M. Holt
Companies: Sam Houston State University and Sam Houston State University
Keywords: Bayesian Joinpoint Regression; Poisson counts; Age-stratified; Lung Cancer ; Incidence; Surveillance Epidemiology and End Results (SEER) Program
Abstract:

Cancer is a major public health problem in the United States and around the globe. Lung cancer is particular concern as the leading cause of cancer-related deaths in males and the second in females. According the Center for Disease Control, 80 to 90 percent of lung cancer incidence is associated with cigarette smoking. Smokers are 15 to 30 times more likely to get lung cancer or die from lung cancer compared to non-smokers. The aim of this study is to develop the quantitative relationship between the smoking rate and incidence of lung cancer in the population. In this study, we develop a Bayesian joinpoint statistical model to jointly study the effect of smoking in the incidence of lung cancer and explore the relationship between them in the time trend. The resulting model estimates and predicts the rate of change of incidence in the time trend with the adjustment of smoking rate in the population along with other applicable covariates including age and gender.


Authors who are presenting talks have a * after their name.

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