Rachel Carroll1, Andrew B Lawson2, Christel Faes3, Russell S Kirby4, Mehreteab Aregay2, Kevin Watjou3. 1. Department of Public Health, Medical University of South Carolina, Charleston. Electronic address: mosra@musc.edu. 2. Department of Public Health, Medical University of South Carolina, Charleston. 3. Interuniversity Institute for Statistics and Statistical Bioinformatics, Hasselt University, Diepenbeek, Belgium. 4. Department of Community and Family Health, University of South Florida, Tampa.
Abstract
PURPOSE: Many types of cancer have an underlying spatial incidence distribution. Spatial model selection methods can be useful when determining the linear predictor that best describes incidence outcomes. METHODS: In this article, we examine the applications and benefits of using two different types of spatial model selection techniques, Bayesian model selection and Bayesian model averaging, in relation to colon cancer incidence in the state of Georgia, United States. RESULTS: Both methods produce useful results that lead to the determination that median household income and percent African American population are important predictors of colon cancer incidence in the Northern counties of the state, whereas percent persons below poverty level and percent African American population are important in the Southern counties. CONCLUSIONS: Of the two presented methods, Bayesian model selection appears to provide more succinct results, but applying the two in combination offers even more useful information into the spatial preferences of the alternative linear predictors.
PURPOSE: Many types of cancer have an underlying spatial incidence distribution. Spatial model selection methods can be useful when determining the linear predictor that best describes incidence outcomes. METHODS: In this article, we examine the applications and benefits of using two different types of spatial model selection techniques, Bayesian model selection and Bayesian model averaging, in relation to colon cancer incidence in the state of Georgia, United States. RESULTS: Both methods produce useful results that lead to the determination that median household income and percent African American population are important predictors of colon cancer incidence in the Northern counties of the state, whereas percent persons below poverty level and percent African American population are important in the Southern counties. CONCLUSIONS: Of the two presented methods, Bayesian model selection appears to provide more succinct results, but applying the two in combination offers even more useful information into the spatial preferences of the alternative linear predictors.
Authors: Kevin A Henry; Recinda L Sherman; Kaila McDonald; Christopher J Johnson; Ge Lin; Antoinette M Stroup; Francis P Boscoe Journal: J Cancer Epidemiol Date: 2014-08-03