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NIMBLE for Bayesian Disease Mapping.

Andrew B Lawson1

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

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Summary
This summary is machine-generated.

This tutorial introduces Bayesian hierarchical models for spatial health data using the R package nimble. It demonstrates implementing custom samplers and fitting Bayesian Disease Mapping models with real-world health data.

Keywords:
BDMBHMBYMBayesianGamma poissonLog normalNimbleconvolution

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Area of Science:

  • Statistical modeling
  • Spatial epidemiology
  • Computational statistics

Background:

  • Bayesian hierarchical models are powerful tools for analyzing complex spatial health data.
  • The R package nimble offers a flexible system for implementing and customizing these models.
  • Existing methods may lack the flexibility needed for advanced spatial health analyses.

Purpose of the Study:

  • To provide a tutorial on implementing Bayesian hierarchical models for spatial health data using the R package nimble.
  • To demonstrate the capabilities of nimbleFunctions for creating custom samplers and optimizing model fitting.
  • To explore Bayesian Disease Mapping models and discuss various analysis approaches.

Main Methods:

  • Utilizing the R package nimble for building and fitting Bayesian hierarchical models.
  • Implementing custom MCMC samplers through nimbleFunctions and C++ compilation.
  • Applying Bayesian Disease Mapping models to publicly available small area health datasets.

Main Results:

  • Demonstrated the practical implementation of Bayesian Disease Mapping models using nimble.
  • Showcased the flexibility of nimble in customizing MCMC samplers and model components.
  • Provided examples of model fitting and analysis for spatial health data.

Conclusions:

  • The R package nimble facilitates the implementation and customization of Bayesian hierarchical models for spatial health data.
  • Nimble offers a powerful and flexible framework for advanced statistical modeling in epidemiology.
  • The tutorial provides a valuable resource for researchers applying spatial health analysis techniques.