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Approximation to the optimal allocation for response adaptive designs.

Yanqing Yi1, Xikui Wang2

  • 1Faculty of Medicine, Memorial University of Newfoundland, St. John's, NL, Canada.

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Summary
This summary is machine-generated.

This study introduces an optimal algorithm for response-adaptive clinical trials, improving patient allocation to better treatments. The method enhances efficiency and statistical power while minimizing unfavorable trial directions.

Keywords:
62P10Adaptive randomizationPrimary 62L05Thompson samplingaverage reward criterionmarkov decision processstatistical power

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Area of Science:

  • Clinical Trials
  • Biostatistics
  • Machine Learning in Healthcare

Background:

  • Response-adaptive clinical trials offer potential advantages over traditional designs.
  • Optimizing treatment allocation is crucial for maximizing trial efficiency and patient benefit.
  • Existing methods may not fully leverage adaptive strategies for optimal reward.

Purpose of the Study:

  • To develop and evaluate an optimal allocation design for response-adaptive clinical trials.
  • To improve treatment allocation strategies using a Markov decision process framework.
  • To enhance the average reward criterion in clinical trial design.

Main Methods:

  • Formulated treatment randomization as a Markov decision process.
  • Employed Bayesian methods for summarizing treatment effect information.
  • Introduced a span-contraction operator to identify optimal policies.
  • Proposed a Thompson sampling algorithm combined with the contraction operator for approximate optimal allocation.

Main Results:

  • Simulations with binary responses (N=200) showed efficient learning, allocating more patients to the superior treatment.
  • The method maintained good statistical power with a low probability (<1.5%) of unfavorable trial direction for detecting a 0.2 difference.
  • For normally distributed responses (N=100), the proposed method assigned 13% more patients to the better treatment compared to complete randomization for an effect size of 0.8, with <0.7% unfavorable trial probability.

Conclusions:

  • The proposed algorithm effectively approximates optimal treatment allocation in response-adaptive trials.
  • The method demonstrates efficient learning, improved patient allocation, and maintained statistical validity.
  • This approach offers a robust strategy for optimizing clinical trial outcomes and resource utilization.