Bayesian model selection methods in modeling small area colon cancer incidence

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• 作者：Rachel Carroll ; PhD^a ; ^{mosra@musc.edu" class="auth_mail" title="E-mail the corresponding author} ; Andrew B. Lawson ; PhD^a ; Christel Faes ; PhD^b ; Russell S. Kirby ; PhD ; MS ; FACE^c ; Mehreteab Aregay ; PhD^a ; Kevin Watjou ; MS^b
• 关键词：Colon cancer ; Bayesian model averaging ; Bayesian model selection ; Spatial regression ; MCMC
• 刊名：Annals of Epidemiology
• 出版年：2016
• 出版时间：January 2016
• 年：2016
• 卷：26
• 期：1
• 页码：43-49
• 全文大小：1567 K

文摘

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.