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  11. On The Finite-sample And Asymptotic Error Control Of A Randomization-probability Test For Response-adaptive Clinical Trials

On the finite-sample and asymptotic error control of a randomization-probability test for response-adaptive clinical trials

Nina Deliu1,2, Sofia S Villar2

  • 1MEMOTEF Department, Sapienza University of Rome, 00161, Rome, Italy.

Biometrics
|June 13, 2025

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View abstract on PubMed

Summary
This summary is machine-generated.

Response-adaptive designs offer optimized outcomes but challenge inference. This study introduces a novel test statistic for adaptive designs, ensuring type-I error control and power efficiency in clinical trials.

Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Statistical Inference

Background:

  • Response-adaptive designs can optimize trial outcomes but present inferential challenges.
  • Lack of type-I error guarantees and power efficiency hinders practical application in clinical trials.

Purpose of the Study:

  • To address the inferential challenges of response-adaptive designs.
  • To develop a novel test statistic with finite-sample and asymptotic guarantees.
  • To evaluate theoretical properties for Thompson sampling in adaptive designs.

Main Methods:

  • Defined a novel test statistic based on randomization probabilities in adaptive designs.
  • Derived finite-sample and asymptotic guarantees for the proposed test statistic.
  • Evaluated theoretical properties of Thompson sampling, a Bayesian adaptive design.
Keywords:
Thompson samplingadaptive designsexact testhypothesis testing

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Main Results:

  • The proposed approach provides frequentist error control advantages.
  • The method preserves expected outcome optimalities.
  • Demonstrated advantages in a phase-II oncology trial and simulations.

Conclusions:

  • The novel test statistic offers robust type-I error control and power efficiency for adaptive designs.
  • This approach enhances the practical utility of response-adaptive designs in clinical research.
  • The findings support the use of adaptive designs with guaranteed inferential properties.
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