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Bayesian adaptive bandit-based designs using the Gittins index for multi-armed trials with normally distributed
Adam L Smith1, Sofía S Villar2
1Department of Pure Mathematics and Mathematical Statistics, University of Cambridge, Cambridge, UK.
Journal of Applied Statistics
|March 20, 2018
Summary
This study introduces near-optimal adaptive designs for clinical trials using Multi-Armed Bandit theory. It addresses challenges in balancing patient benefit with statistical rigor, especially for normally distributed endpoints.
Area of Science:
- Clinical Trials
- Biostatistics
- Medical Research
Background:
- Adaptive designs enhance clinical trial efficiency and patient outcomes.
- Developing response-adaptive designs presents challenges in maintaining statistical rigor and unbiased comparisons.
Purpose of the Study:
- To define near-optimal adaptive designs for multi-armed clinical trials with normally distributed endpoints using Multi-Armed Bandit theory.
- To evaluate the operating characteristics and patient benefit of these designs.
Main Methods:
- Application of Multi-Armed Bandit problem theory to adaptive clinical trial design.
- Simulation studies to assess operating characteristics (type I error, power, bias) and patient benefit.
- Comparison with designs for Bernoulli endpoints.
Main Results:
- Bandit-based adaptive designs offer near-optimal solutions for normally distributed endpoints.
- Comparison with Bernoulli endpoints reveals similarities and key differences, particularly in type I error control.
- Observed type I error inflation in adaptive rules was identified.
Conclusions:
- Adaptive designs, informed by Multi-Armed Bandit theory, can improve clinical trials but require careful type I error control.
- A simulation-based testing procedure is proposed to correct for type I error inflation.
- The findings offer practical insights for designing more effective and ethical clinical trials.
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