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Related Concept Videos

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Optimising error rates in programmes of pilot and definitive trials using Bayesian statistical decision theory.

Duncan T Wilson1, Andrew Hall1, Julia M Brown1

  • 1Leeds Institute of Clinical Trials Research, University of Leeds, UK.

Statistical Methods in Medical Research
|April 1, 2025
PubMed
Summary

Pilot trials can be optimized for effectiveness testing using a Bayesian approach. This method balances statistical power and error rates to maximize expected utility, improving clinical trial design.

Keywords:
Clinical trialexpected utilityexternal pilotoptimal designpilot trialstatistical decision theory

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Area of Science:

  • Clinical Trial Design
  • Bayesian Statistics
  • Health Economics

Background:

  • Pilot trials inform definitive trial design but typically avoid effectiveness testing due to low power.
  • Methodological guidance for balancing power and Type I error in pilot trials is limited.
  • Conventional pilot trial approaches may be suboptimal for maximizing overall program utility.

Purpose of the Study:

  • To develop a Bayesian decision-theoretic framework for optimizing pilot and definitive trial programs.
  • To define an optimal trial program based on maximizing expected utility, considering various cost and risk factors.
  • To re-design the OK-Diabetes pilot trial using this novel approach.

Main Methods:

  • A Bayesian decision-theoretic approach was employed, incorporating a utility function.
  • Utility was defined based on primary outcome changes, sampling costs, treatment expenses, and risk tolerance.
  • The framework was applied to re-design the OK-Diabetes pilot trial with a continuous primary outcome.

Main Results:

  • The proposed Bayesian approach provides a method to determine optimal operating characteristics for pilot and definitive trials.
  • Analysis of the OK-Diabetes trial demonstrated how optimal program features change with utility function parameters.
  • The study found that not testing for effectiveness in pilot trials can be significantly suboptimal.

Conclusions:

  • A Bayesian decision-theoretic framework offers a superior method for designing combined pilot and definitive trial programs.
  • This approach allows for a more informed balance between statistical power and error rates in early-phase trials.
  • Optimizing pilot trials for effectiveness testing can lead to more efficient and informative research programs.