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Sample sizes for phase II clinical trials derived from Bayesian decision theory

H C Brunier1, J Whitehead

  • 1Department of Applied Statistics, The University, Reading, U.K.

Statistics in Medicine
|December 15, 1994
PubMed
Summary

This study proposes a decision theory approach to optimize sample sizes in early phase clinical trials. It balances treatment benefits against costs, considering future studies and potential errors for better trial planning.

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Area of Science:

  • Clinical Trials
  • Decision Theory
  • Biostatistics

Background:

  • Early phase clinical trials are crucial for evaluating new medical treatments.
  • Determining optimal sample size is essential for efficient trial progression.
  • Existing methods may not fully account for future study implications and costs.

Purpose of the Study:

  • To propose a decision theoretic approach for determining optimal sample size in early phase clinical trials.
  • To incorporate 'gained successes' and treatment costs into sample size calculations.
  • To extend existing methodologies by adopting a Bayesian formulation and considering later study outcomes.

Main Methods:

  • Utilizing a decision theoretic framework to maximize a defined utility function.
  • Adopting a Bayesian approach to integrate prior knowledge and update beliefs.

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  • Incorporating costs of treatment and potential gains from successful outcomes.
  • Accounting for sample sizes in subsequent studies and routine use.

    • Main Results:

    • The proposed method provides a framework for optimizing sample size by balancing potential benefits and costs.
    • The utility function effectively integrates diverse factors influencing trial decisions.
    • The Bayesian formulation allows for adaptive and informed sample size determination.
    • The model considers the impact of potential erroneous conclusions in later trials.

    Conclusions:

    • The decision theoretic approach offers a robust method for sample size determination in early phase trials.
    • This approach enhances the efficiency and informativeness of early clinical investigations.
    • Considering future study implications and costs leads to more pragmatic trial designs.
    • The methodology provides a valuable tool for optimizing resource allocation in medical research.