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Bayesian and mixed Bayesian/likelihood criteria for sample size determination

L Joseph1, R du Berger, P Bélisle

  • 1Montreal General Hospital, Department of Medicine, Quebec, Canada.

Statistics in Medicine
|April 15, 1997
PubMed
Summary
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Accurate sample size estimation in medical research requires using prior information as distributions, not just point estimates. Bayesian methods offer improved sample size calculations for experiments, particularly for comparing binomial proportions.

Area of Science:

  • Biostatistics
  • Medical Research Design
  • Statistical Inference

Background:

  • Sample size estimation is critical for designing medical experiments.
  • Current methods often use limited prior information (point estimates).
  • Prior information is more accurately represented as a distribution of values.

Purpose of the Study:

  • To review Bayesian and mixed Bayesian/likelihood approaches for sample size calculations.
  • To apply these methods to estimate the difference between two binomial proportions.
  • To compare Bayesian results with standard sample size formulae.

Main Methods:

  • Review of Bayesian and mixed Bayesian/likelihood statistical methods.
  • Application of these methods to sample size determination for comparing two binomial proportions.

Related Experiment Videos

  • Comparison of sample size estimates derived from Bayesian and standard approaches.

    • Main Results:

    • Bayesian approaches utilize prior information more effectively by considering distributions.
    • Sample size calculations based on posterior credible intervals offer an alternative to standard formulae.
    • The study provides a comparative analysis of different sample size estimation strategies.

    Conclusions:

    • Bayesian and mixed methods provide a more nuanced approach to sample size estimation in medical research.
    • Considering distributions of prior information can lead to more robust experimental designs.
    • Selection of an appropriate sample size involves evaluating multiple criteria for optimal experimental planning.