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Posterior predictive model checks for disease mapping models.

H S Stern1, N Cressie

  • 1Department of Statistics, Iowa State University, Ames, IA 50011-1210, USA. hstern@iastate.edu

Statistics in Medicine
|August 29, 2000
PubMed
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Bayesian disease mapping uses models to create reliable small-area rate maps. This study proposes cross-validation posterior predictive distributions to check if extreme disease rates are real findings or model errors.

Area of Science:

  • Epidemiology
  • Biostatistics
  • Geographic Information Systems (GIS)

Background:

  • Small-area disease incidence and mortality rates are often mapped for public health surveillance.
  • Raw rate maps are unreliable due to population size variations, leading to model-based approaches like Bayesian methods.
  • Accurate disease risk assessment is crucial for epidemiological and political decision-making.

Purpose of the Study:

  • To scrutinize the assumptions of statistical models used in small-area disease mapping.
  • To differentiate between genuine epidemiological findings and potential model fit issues in disease rate extrema.
  • To introduce and evaluate a cross-validation posterior predictive distribution for model assessment.

Main Methods:

  • Review of posterior predictive model checks for assessing statistical model fitness in disease mapping.

Related Experiment Videos

  • Proposal of a cross-validation posterior predictive distribution method, excluding suspect areas for reanalysis.
  • Description of two approximation methods for the cross-validation posterior predictive distribution in large datasets.

    • Main Results:

    • Posterior predictive checks are essential for evaluating the fit of disease mapping models.
    • The proposed cross-validation method helps determine if observed extreme disease counts are consistent with the model.
    • Approximation techniques are provided for computational feasibility in large-scale analyses.

    Conclusions:

    • Model-based disease mapping requires rigorous validation of underlying assumptions.
    • The cross-validation posterior predictive distribution offers a robust approach to assess model performance and identify true disease hotspots.
    • Validated mapping techniques are vital for reliable public health surveillance and resource allocation.