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Bayesian analysis of case-control studies.

R J Marshall1

  • 1Department of Community Health, University of Auckland, New Zealand.

Statistics in Medicine
|December 1, 1988
PubMed
Summary
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This study introduces a Bayesian method for estimating odds ratios in case-control studies. The approach provides exact posterior densities and moments, with a practical log-normal approximation.

Area of Science:

  • Biostatistics
  • Epidemiology
  • Statistical Inference

Background:

  • Case-control studies are crucial for investigating disease etiology.
  • Estimating the odds ratio is fundamental in analyzing case-control data.
  • Bayesian methods offer a flexible framework for statistical inference.

Purpose of the Study:

  • To develop and evaluate a Bayesian approach for odds ratio estimation.
  • To derive the exact posterior distribution and moments of the odds ratio.
  • To assess the utility of a log-normal approximation for practical applications.

Main Methods:

  • Utilized a Bayesian framework for odds ratio estimation.
  • Derived the exact posterior density and moments of the odds ratio.
  • Investigated a log-normal approximation for the posterior density.

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Main Results:

  • The Bayesian method yields exact posterior densities and moments for the odds ratio.
  • A log-normal approximation to the posterior density is demonstrated to be adequate.
  • Methods for setting prior parameters are discussed with illustrative examples.

Conclusions:

  • The proposed Bayesian approach provides a robust method for odds ratio estimation.
  • The log-normal approximation simplifies practical implementation without significant loss of accuracy.
  • Guidance on prior parameter selection enhances the applicability of the method.