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  3. Sample Sizes For Randomized Controlled Trials Utilizing Bayesian Response Adaptive Randomization For Continuous Outcomes.
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  3. Sample Sizes For Randomized Controlled Trials Utilizing Bayesian Response Adaptive Randomization For Continuous Outcomes.

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Sample sizes for randomized controlled trials utilizing Bayesian response adaptive randomization for continuous

Vahan Aslanyan1,2, Michelle Nuño1,3, Trevor A Pickering1

  • 1Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.

Journal of Biopharmaceutical Statistics
|June 11, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces a Bayesian method for estimating sample sizes in multi-arm trials using response adaptive randomization (RAR). Early interim analyses can reduce participant numbers, though RAR may increase sample size for large effects.

Keywords:
Adaptive designresponse adaptive randomizationsample size re-estimation

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Statistical Modeling

Background:

  • Sample size estimation is crucial for multi-arm randomized controlled trials (RCTs).
  • Response adaptive randomization (RAR) dynamically adjusts treatment allocation based on accumulating data.
  • Bayesian approaches offer a flexible framework for complex trial designs.

Purpose of the Study:

  • To present a Bayesian approach for sample size estimation in multi-arm RCTs with continuous outcomes using RAR.
  • To compare sample size requirements between hypothesis testing and Bayesian methods under different interim analysis scenarios.
  • To evaluate the impact of RAR on sample size estimation, particularly with large treatment effects.

Main Methods:

  • Developed a Bayesian framework incorporating outcome data to update treatment effect estimates and modify allocation proportions.
  • Simulated a 4-arm trial to compare sample sizes from hypothesis testing versus Bayesian approaches with and without interim analyses and RAR.
  • Applied the proposed method to two completed RCTs to validate findings.

    • Main Results:

    • Two interim analyses (at 25% and 50% enrollment) can reduce the overall number of participants needed.
    • RAR-based sample size estimates can increase, especially when treatment effects are large, due to imbalanced allocation.
    • Bayesian approach with interim analyses and RAR showed potential for participant reduction compared to non-adaptive designs.

    Conclusions:

    • Early and frequent interim analyses can decrease the sample size required for conclusive trials.
    • The benefits of within-trial patient allocation to effective treatments via RAR must be weighed against potential increases in sample size requirements.
    • The proposed Bayesian approach provides a robust method for sample size determination in adaptive RCTs.