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Bayesian multi-scale modeling for aggregated disease mapping data.

Mehreteab Aregay1, Andrew B Lawson1, Christel Faes2

  • 11 Department of Public Heath Sciences, Division of Biostatistics and Bioinformatics, MUSC, Charleston, USA.

Statistical Methods in Medical Research
|October 1, 2015
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Summary
This summary is machine-generated.

This study introduces hierarchical Bayesian models to address the scale effect in disease mapping. Models with shared random effects demonstrated superior performance in capturing spatial patterns across different data resolutions.

Keywords:
Deviance information criterionWatanabe-Akaike information criterionpredictive accuracyscaling effectshared random effect model

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Area of Science:

  • Spatial statistics
  • Biostatistics
  • Geographic information systems

Background:

  • The scale effect, arising from data aggregation across different spatial resolutions, is a common challenge in disease mapping.
  • Hierarchical modeling offers a robust framework for addressing complex spatial data structures.

Purpose of the Study:

  • To develop and evaluate novel multiscale hierarchical Bayesian models for disease mapping.
  • To investigate the impact of shared random effects versus independent models on spatial data at different resolutions.

Main Methods:

  • Proposed four distinct multiscale hierarchical Bayesian models, including those with shared random effects and independent convolution models.
  • Employed deviance information criterion (DIC) and Watanabe-Akaike information criterion (WAIC) for model comparison.
  • Validated models using both simulated and real-world disease mapping data.

Main Results:

  • Models incorporating shared random effects, where finer levels inherit spatial information from coarser levels, consistently outperformed other proposed models.
  • The performance advantage was observed across various criteria and data types.

Conclusions:

  • Hierarchical Bayesian models with shared random effects are effective in mitigating the scale effect in disease mapping.
  • These models provide a more accurate representation of spatial disease risk across multiple resolutions.