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Hierarchical multivariate directed acyclic graph autoregressive models for spatial diseases mapping.

Leiwen Gao1, Abhirup Datta2, Sudipto Banerjee1

  • 1Department of Biostatistics, University of California, Los Angeles, Los Angeles, California, USA.

Statistics in Medicine
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Summary
This summary is machine-generated.

This study introduces new statistical models for mapping multiple diseases simultaneously. The methods help separate disease associations from geographic patterns, improving understanding of environmental risk factors.

Keywords:
Bayesian hierarchical modelsareal data analysisdirected acyclic graphical autoregressionmultiple disease mappingmultivariate areal data models

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Geographic Information Systems (GIS)

Background:

  • Disease mapping uses spatial models to analyze geographic variations in disease rates and identify environmental risk factors.
  • Multivariate disease mapping analyzes multiple diseases within geographic regions, but disentangling disease associations from spatial patterns is challenging.

Purpose of the Study:

  • To develop and validate statistical models for multivariate disease mapping that can separate spatial autocorrelation from inter-disease associations.
  • To provide a flexible and interpretable framework for analyzing multiple disease outcomes in geographically aggregated data.

Main Methods:

  • Development of multivariate directed acyclic graphical autoregression models to account for spatial and inter-disease dependencies.
  • Utilizing Bayesian model selection and averaging across modeling orders via bridge sampling to address model dependency on cancer ordering.
  • Comparison with existing methods through simulation studies.

Main Results:

  • The proposed models effectively disentangle spatial autocorrelation from inter-disease associations in multivariate disease mapping.
  • Bayesian model selection and averaging provide a robust approach to handle model order dependence.
  • The methods are successfully applied to multiple cancer mapping using real-world data.

Conclusions:

  • The developed multivariate graphical autoregression models offer a powerful tool for disease mapping, enhancing the understanding of complex disease relationships and spatial patterns.
  • The Bayesian approach ensures flexibility and interpretability in analyzing multiple diseases across geographic regions.
  • This work advances the field of spatial epidemiology by providing improved methods for analyzing geographically aggregated health data.