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Two Stage Designs for Phase III Clinical Trials
Dean Follmann1, Michael Proschan1
1Biostatistics Research Branch, National Institute of Allergy and Infectious Diseases, 5601 Fishers Lane, Bethesda MD 20892, U.S.A.
Medrxiv : the Preprint Server for Health Sciences
|August 15, 2020
Summary
Phase III platform trials can be improved using a two-stage design, maximizing the ratio of expected wins (RW) for evaluating sequential treatments. This approach enhances efficiency in identifying efficacious therapies, including for COVID-19.
Area of Science:
- Clinical Trials Methodology
- Biostatistics
- Epidemiology
Background:
- Phase III platform trials are crucial for evaluating sequential treatments for diseases.
- Traditional trial designs often prioritize sequential questions over overall enterprise performance.
- Optimizing platform trial efficiency is essential for rapid therapeutic development.
Approach:
- Developed a two-stage trial framework with early evaluation to determine study continuation.
- Introduced the ratio of expected wins (RW) metric to compare two-stage vs. standard Phase III trial performance.
- Utilized Brownian Motion to approximate test statistics and optimize stage 1 parameters (time and type I error) for maximizing RW.
Key Points:
- The two-stage approach significantly improves the expected number of reported efficacious treatments compared to standard Phase III trials.
- Optimized stage 1 decision rules maximize the ratio of expected wins (RW) under fixed power.
- Incorporating high-quality surrogate endpoints at stage 1 further enhances trial performance and efficiency.
Conclusions:
- The proposed two-stage platform trial design offers a more efficient method for evaluating multiple treatments.
- This framework is particularly beneficial when using surrogate endpoints for early efficacy assessment.
- The methodology is applicable to designing platform trials for diseases like COVID-19.
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