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The sample size for a clinical trial: a Bayesian-decision theoretic approach.

J Halpern1, B W Brown, J Hornberger

  • 1Stanford University School of Medicine, Department of Health Research and Policy, HRP Redwood Building, Stanford, California 94305-5405, USA. funn@stanford.edu

Statistics in Medicine
|March 17, 2001
PubMed
Summary

This study provides a program to determine appropriate clinical trial sample sizes for binary endpoints using decision theory. It implements Canner

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Decision Theory

Background:

  • Determining appropriate sample size is crucial for clinical trial validity.
  • Existing methods may not fully integrate decision-theoretic principles for binary endpoints.

Purpose of the Study:

  • To present a practical program for calculating clinical trial sample sizes based on decision theory.
  • To extend Canner's solution for sample size determination with binary outcomes.
  • To compare Bayesian and Neyman-Pearson approaches in this context.

Main Methods:

  • Utilizing decision theory to formulate the sample size problem.
  • Implementing an extended version of Canner's solution through a software program.
  • Applying the program with illustrative examples.

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Main Results:

  • The developed program offers a practical tool for clinical trial sample size planning.
  • The study demonstrates the application of the program with concrete examples.
  • Discussion highlights the implications of a Bayesian approach.

Conclusions:

  • The presented decision-theoretic framework and program provide a robust method for sample size determination in clinical trials with binary endpoints.
  • Comparing Bayesian and Neyman-Pearson approaches offers insights for trial design.
  • The tool facilitates informed planning for clinical research.