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Related Experiment Videos

Estimation in Bayesian disease mapping.

Ying C MacNab1, Patrick J Farrell, Paul Gustafson

  • 1Division of Epidemiology and Biostatistics, Department of Health Care and Epidemiology, University of British Columbia, British Columbia V6T 1Z3, Canada. ymacnab@cw.bc.ca

Biometrics
|December 21, 2004
PubMed
Summary
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Fully Bayesian (FB) inference offers more accurate uncertainty estimates for small-area relative risks than empirical Bayes (EB) methods. This study introduces an EB bootstrap methodology that improves EB confidence intervals for disease mapping.

Area of Science:

  • Biostatistics
  • Spatial Epidemiology
  • Statistical Modeling

Background:

  • Empirical Bayes (EB) methods for disease mapping may underestimate uncertainty in relative risk estimates.
  • Fully Bayesian (FB) approaches are suggested to better capture uncertainty by accounting for hyperparameter variability.

Purpose of the Study:

  • To develop and illustrate an empirical Bayes (EB) bootstrap methodology for relative risk inference.
  • To contrast this EB bootstrap approach with hyperprior Bayes and fully Bayesian (FB) methods.
  • To assess the performance of parametric bootstrap procedures in adjusting for undercoverage in naive EB intervals.

Main Methods:

  • Development and illustration of an EB bootstrap methodology for relative risk inference.
  • Comparison of FB inference (via Markov chain Monte Carlo) with EB inference (via penalized quasi-likelihood).

Related Experiment Videos

  • Parametric bootstrap procedures to correct for undercoverage in EB confidence intervals.

    • Main Results:

    • The EB bootstrap methodology provides accurate parametric EB confidence intervals.
    • A close connection is elucidated between the EB bootstrap methodology and hyperprior Bayes.
    • Parametric bootstrap procedures effectively adjust for undercoverage in naive EB interval estimates.

    Conclusions:

    • Both FB and EB methods play crucial roles in disease risk inference and map interpretation.
    • The developed EB bootstrap methodology offers an improved approach for accurate confidence intervals in small-area analyses.
    • Findings are motivated by small-area infant mortality rate analysis in British Columbia, Canada.