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Bayesian model selection methods in modeling small area colon cancer incidence.

Rachel Carroll1, Andrew B Lawson1, Christel Faes2

  • 1Department of Public Health, Medical University of South Carolina, Charleston.

Annals of Epidemiology
|December 22, 2015
PubMed
Summary

Bayesian methods reveal that income and race predict colon cancer in Northern Georgia, while poverty and race predict it in Southern Georgia. Combining techniques offers deeper insights into spatial cancer incidence.

Keywords:
Bayesian model averagingBayesian model selectionColon cancerMCMCSpatial regression

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Spatial Analysis

Background:

  • Cancer incidence often exhibits distinct spatial patterns.
  • Accurate spatial modeling is crucial for understanding disease distribution.

Purpose of the Study:

  • To evaluate Bayesian model selection and Bayesian model averaging for spatial analysis of colon cancer incidence.
  • To identify key predictors of colon cancer across different regions in Georgia.

Main Methods:

  • Applied Bayesian model selection and Bayesian model averaging techniques.
  • Analyzed colon cancer incidence data in Georgia, USA.
  • Investigated the influence of socioeconomic and demographic factors.

Main Results:

  • Identified median household income and African American population as significant predictors in Northern Georgia.
  • Determined poverty level and African American population as key predictors in Southern Georgia.
  • Both Bayesian model selection and averaging provided valuable, yet distinct, insights.

Conclusions:

  • Bayesian model selection offers concise results for spatial predictor identification.
  • Combining Bayesian model selection and averaging enhances understanding of spatial incidence patterns.
  • Socioeconomic and demographic factors play regionally specific roles in colon cancer incidence.