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An efficient Bayesian platform trial design for borrowing adaptively from historical control data in lymphoma.
James Normington1, Jiawen Zhu2, Federico Mattiello3
1Division of Biostatistics, School of Public Health, University of Minnesota, USA.
Contemporary Clinical Trials
|November 20, 2019
Summary
This study introduces a Bayesian adaptive platform trial design for oncology, enabling ethical and economical drug development by borrowing data from historical controls. The innovative approach improves efficiency and reduces patient numbers in clinical trials.
Area of Science:
- Clinical Trials
- Biostatistics
- Oncology Drug Development
Background:
- Clinical trials face high costs and ethical risks.
- Borrowing data from similar completed trials can reduce patient numbers.
- Lymphoma drug development presents competing therapies.
Purpose of the Study:
- Propose a Bayesian adaptive platform trial design.
- Utilize commensurate priors for interim analyses.
- Adaptively borrow information from historical control groups.
Main Methods:
- Bayesian adaptive platform trial design.
- Commensurate prior methods at interim analyses.
- Simulation studies to evaluate performance.
Main Results:
- The design adjusts randomization ratios favoring novel treatments.
- Effectively supplements control arms with historical data.
- Performs well across varying commensurability and treatment effects.
Conclusions:
- The proposed design is ethical and economical for oncology trials.
- Shortens the time to market for new treatments.
- Outperforms adaptive 'all-or-nothing' approaches.
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