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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/20629

Title: Bayesian model selection methods in modeling small area colon cancer incidence
Authors: Carroll, Rachel
Lawson, Andrew B.
Faes, Christel
Kirby, Russell S.
Aregay, Mehreteab
Watjou, Kevin
Issue Date: 2016
Publisher: ELSEVIER SCIENCE INC
Citation: ANNALS OF EPIDEMIOLOGY, 26 (1), p. 43-49
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. (C) 2016 Elsevier Inc. All rights reserved.
Notes: [Carroll, Rachel; Lawson, Andrew B.; Aregay, Mehreteab] Med Univ S Carolina, Dept Publ Hlth, Charleston, SC 29425 USA. [Faes, Christel; Watjou, Kevin] Hasselt Univ, Interuniv Inst Stat & Stat Bioinformat, Diepenbeek, Belgium. [Kirby, Russell S.] Univ S Florida, Dept Community & Family Hlth, Tampa, FL USA.
URI: http://hdl.handle.net/1942/20629
DOI: 10.1016/j.annepidem.2015.10.011
ISI #: 000367420100007
ISSN: 1047-2797
Category: A1
Type: Journal Contribution
Validation: ecoom, 2017
Appears in Collections: Research publications

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