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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
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.
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.
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