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Related Experiment Video

Updated: Apr 3, 2026

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A Bayesian Hybrid Adaptive Randomisation Design for Clinical Trials with Survival Outcomes.

M Moatti, S Chevret1, S Zohar

  • 1Sylvie Chevret, Biostatistics and Clinical Epidemiology (ECSTRA) Team, Paris Diderot University, Saint-Louis hospital, 1, avenue Claude Vellefaux, 75475 Paris Cedex 10, France,

Methods of Information in Medicine
|September 26, 2015
PubMed
Summary
This summary is machine-generated.

This study introduces a Bayesian hybrid adaptive design for survival trials, optimizing patient allocation to minimize failure hazards. The adaptive approach reduces trial duration and improves ethical outcomes for patients in clinical research.

Keywords:
Adaptive designBayesiancensored dataoptimal allocationresponse-adaptive randomisation

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Area of Science:

  • Clinical Trials Methodology
  • Biostatistics
  • Survival Analysis

Background:

  • Response-adaptive randomization designs enhance clinical trial efficiency and patient outcomes.
  • Existing methods for failure time outcomes target optimal allocation with fixed sample sizes.

Purpose of the Study:

  • Propose a response-adaptive randomization procedure for survival trials with interim monitoring.
  • Identify the optimal allocation that minimizes expected failure hazard for a fixed variance of the estimated log hazard ratio.
  • Demonstrate the design's utility by redesigning a multiple myeloma clinical trial.

Main Methods:

  • Utilize a Bayesian response-adaptive randomization procedure for continuous data monitoring.
  • Employ the log hazard ratio as the primary effect measure.
  • Derive optimal target allocation from the posterior estimate of the log hazard ratio, combining prior and normal likelihood.
  • Compare the proposed Bayesian hybrid adaptive design against fixed, sequential, and other adaptive designs via simulation.
  • Incorporate stopping rules based on the posterior distribution of the log hazard ratio.

Main Results:

  • Adaptive designs showed a reduction in observed deaths compared to non-adaptive designs.
  • A Bayes mixture prior maximized the reduction in observed deaths.
  • Fully Bayesian procedures did not offer a clear-cut improvement over Bayes mixture priors.
  • Stopping rules slightly decreased the observed proportion of deaths under the alternate hypothesis compared to adaptive designs without stopping rules.

Conclusions:

  • Bayesian hybrid adaptive survival trials offer a promising alternative to traditional designs.
  • These adaptive trials can reduce overall trial duration.
  • Adaptive designs optimize ethical considerations for trial participants.