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Related Experiment Videos

Sample size determination for phase II clinical trials based on Bayesian decision theory

N Stallard1

  • 1Medical and Pharmaceutical Statistics Research Unit, University of Reading, U.K. n.stallard@reading.ac.uk

Biometrics
|April 17, 1998
PubMed
Summary

This study applies Bayesian decision theory to optimize sample size in phase II clinical trials. An optimal group sequential design maximizes financial gain, outperforming methods that strictly control error rates.

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Decision Theory

Background:

  • Determining optimal sample size for clinical trials is crucial for efficient drug development.
  • Existing methods may not fully account for financial costs and potential profits.
  • Group sequential designs allow for early stopping of trials based on accumulating data.

Purpose of the Study:

  • To apply Bayesian decision theory for optimal sample size determination in phase II clinical studies.
  • To develop group sequential designs that maximize expected financial gain from drug development programs.
  • To compare a novel optimal design with existing Bayesian approaches.

Main Methods:

  • Utilized backward induction to derive optimal group sequential designs.
  • Defined a gain function incorporating financial costs and potential profits.

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  • Compared the proposed optimal design with the Thall and Simon Bayesian procedure.

    • Main Results:

    • The proposed optimal design yielded a considerably larger expected gain compared to the Thall and Simon method.
    • The Thall and Simon method, while controlling type I error, resulted in suboptimal financial outcomes.
    • Identified specific gain function forms under which the Thall and Simon boundary is optimal.

    Conclusions:

    • Bayesian decision theory provides a robust framework for optimizing sample size in phase II trials.
    • Maximizing expected financial gain leads to more efficient drug development strategies than solely controlling error rates.
    • The proposed gain function and optimal design offer a practical approach for industry decision-making.