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Bayesian Design of Superiority Trials: Methods and Applications.

Wenlin Yuan1, Ming-Hui Chen1, John Zhong2

  • 1Department of Statistics, University of Connecticut at Storrs, CT 06269.

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|March 27, 2023
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Summary

This study introduces Bayesian sample size determination (SSD) for two-arm superiority trials, offering a flowchart and methods for incorporating historical data. It analyzes Bayesian type I error and power, crucial for efficient clinical trial design.

Keywords:
Borrowing-by-Parts Power PriorConditional BorrowingPower PriorSample Size Determination

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Bayesian Statistics

Background:

  • Traditional sample size determination methods may not fully leverage available data.
  • Bayesian approaches offer flexibility in clinical trial design, including the incorporation of historical data.

Purpose of the Study:

  • To present a practical roadmap for Bayesian sample size determination (SSD) in two-arm superiority clinical trials.
  • To introduce a novel borrowing-by-parts power prior for enhanced historical data utilization.
  • To formally develop conditional borrowing within a decision rule framework.

Main Methods:

  • Development of a flowchart for Bayesian SSD.
  • Empirical examination of borrowing, noninformative priors, and model misspecification effects.
  • Formulation of conditional borrowing and a new borrowing-by-parts power prior.
  • Development of computational algorithms for Bayesian type I error and power calculation.

Main Results:

  • The study provides a structured approach to Bayesian SSD for superiority trials.
  • Analysis reveals the impact of borrowing, prior choices, and model misspecification on trial operating characteristics.
  • A new prior is proposed for more effective historical data incorporation.

Conclusions:

  • The proposed Bayesian SSD framework offers a practical and statistically rigorous method for designing superiority trials.
  • The developed methods and algorithms facilitate efficient clinical trial design by optimizing the use of historical data and addressing potential misspecification.