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Number of Repetitions in Re-Randomization Tests
Yilong Zhang1, Yujie Zhao2, Bingjun Wang2
1Reality Labs, Meta Platforms Inc., Menlo Park, California, USA.
Pharmaceutical Statistics
|October 16, 2024
Summary
Re-randomization tests offer valid statistical inferences for adaptive randomization but require many repetitions. This study introduces an adaptive procedure to reduce computational burden in clinical trials, making these tests more practical.
Area of Science:
- Biostatistics
- Clinical Trial Design
- Statistical Inference
Background:
- Correlated treatment assignment and outcomes occur in adaptive randomization.
- Re-randomization tests provide valid statistical inferences in such scenarios.
- Group sequential designs in clinical trials can necessitate extremely small significance bounds.
Purpose of the Study:
- Investigate the number of repetitions required for re-randomization tests.
- Address the computational intractability of numerous repetitions.
- Propose and evaluate an adaptive procedure to reduce computational demands.
Main Methods:
- Developed an adaptive procedure to decrease the number of repetitions.
- Compared the proposed procedure with existing approaches.
- Utilized Monte Carlo simulations to assess performance with limited sample sizes.
Main Results:
- The proposed adaptive procedure effectively reduces the number of required repetitions.
- Simulations demonstrate the approach's viability in limited sample size settings.
- Strategies for reducing overall computation time were identified.
Conclusions:
- The adaptive procedure offers a computationally feasible alternative for re-randomization tests.
- Practical guidance is provided for implementing these tests efficiently in clinical trials.
- The findings enhance the utility of re-randomization tests in adaptive clinical trial designs.
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