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Alternative models and randomization techniques for Bayesian response-adaptive randomization with binary outcomes.

Jennifer Proper1, John Connett1, Thomas Murray1

  • 1Division of Biostatistics, University of Minnesota Twin Cities, Minneapolis, MN, USA.

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|April 30, 2021
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Summary
This summary is machine-generated.

This study introduces a new Bayesian response-adaptive design using a logistic model and alternative randomization to improve clinical trial efficiency. The enhanced design offers better type I error control and avoids unfavorable sample size imbalances, making it a more reliable approach.

Keywords:
Clinical trialsgroup sequentiallogistic regressionmass-weighted urn randomizationphase II

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Area of Science:

  • Clinical Trials
  • Biostatistics
  • Medical Research

Background:

  • Bayesian response-adaptive designs can lead to unequal sample sizes and inflated type I error rates.
  • Conventional implementations often use beta-binomial models, with limited examination of alternatives.
  • Practical limitations of standard designs hinder their widespread adoption in clinical trials.

Purpose of the Study:

  • To evaluate a novel Bayesian response-adaptive design using a logistic probability model and alternative randomization methods.
  • To address criticisms of conventional designs, including subject imbalance and type I error inflation.
  • To improve operating characteristics for binary outcomes in clinical trials, inspired by the Advanced R2 trial.

Main Methods:

  • A computer simulation study was conducted to assess the proposed design.
  • Thompson sampling with a logistic regression probability model was employed.
  • Urn or permuted block randomization methods were used to limit deviations from target allocation ratios.

Main Results:

  • The logistic model resulted in smaller average sample sizes and maintained similar power compared to the beta-binomial model.
  • Improved type I error rate control was observed with the logistic model.
  • Alternative randomization methods minimized the risk of sample size imbalance favoring the inferior treatment.

Conclusions:

  • The proposed logistic regression probability model with urn or permuted block randomization significantly enhances Bayesian response-adaptive designs.
  • This improved design offers better type I error rate control and reduces the risk of unfavorable sample size imbalances.
  • The findings suggest a more practical and reliable approach for adaptive clinical trials with binary outcomes.