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Related Experiment Videos

Approximate cross-validatory predictive checks in disease mapping models.

E C Marshall1, D J Spiegelhalter

  • 1Department of Epidemiology and Public Health, Imperial College of Science, Technology and Medicine, Norfolk Place, London W2 1PG, UK. c.marshall@ic.ac.uk

Statistics in Medicine
|April 30, 2003
PubMed
Summary

This study improves model assessment for complex disease mapping. Replicating random effects and data offers a faster, more effective way to identify regions deviating from Bayesian hierarchical models.

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Area of Science:

  • Biostatistics
  • Spatial Epidemiology
  • Bayesian Modeling

Background:

  • Complex hierarchical disease mapping models are crucial for epidemiological studies.
  • Identifying regions that deviate from model assumptions is vital for accurate disease mapping.
  • Traditional leave-one-out cross-validation is computationally intensive with Markov chain Monte Carlo (MCMC) methods.

Purpose of the Study:

  • To improve upon existing importance sampling approximations for assessing complex hierarchical disease mapping models.
  • To develop a more efficient method for identifying regions that diverge from model assumptions.
  • To provide a generic and easily applicable approach for criticizing Bayesian hierarchical models.

Main Methods:

  • The study builds upon the importance sampling approximation proposed by Stern and Cressie.

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  • The proposed method involves replication of both random effects and data within the model fitting process.
  • This approach is designed to be compatible with Markov chain Monte Carlo (MCMC) estimation.

    • Main Results:

    • The proposed method of replicating random effects and data improves upon the standard importance sampling approximation.
    • This enhanced approach provides a more computationally feasible way to perform cross-validatory assessments.
    • The method is shown to be effective in identifying regions that do not fit the assumed hierarchical model.

    Conclusions:

    • Replication of random effects and data offers a practical enhancement to model assessment in Bayesian hierarchical disease mapping.
    • This generic and simple-to-apply method can aid in the critical evaluation of complex spatial epidemiological models.
    • The findings contribute to more robust and reliable disease mapping analyses.