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Related Experiment Video

Updated: Nov 17, 2025

Using Rapid Serial Visual Presentation to Measure Set-Specific Capture, a Consequence of Distraction While Multitasking
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Response-adaptive trial designs with accelerated Thompson sampling.

Jixian Wang1

  • 1Biometrics and Data Science, Bristol Myers Squibb, Boudry, Switzerland.

Pharmaceutical Statistics
|February 15, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces an accelerated Thompson sampling method to improve patient allocation in clinical trials, especially for rare diseases. The new approach enhances treatment response rates and is easily implementable, even for batch allocations.

Keywords:
Gittins indexThompson samplingmachine learningrare disease trialsresponse adaptive design

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Area of Science:

  • Clinical Trials
  • Biostatistics
  • Machine Learning

Background:

  • Optimal patient allocation to treatments is crucial in clinical trials, particularly for rare diseases.
  • The Gittins index rule offers optimal sequential allocation but is complex and lacks randomness.
  • Thompson sampling balances allocation and randomness but shows poor short-run performance in clinical settings.

Purpose of the Study:

  • To enhance the short-run performance of Thompson sampling in clinical trial patient allocation.
  • To introduce a novel, easily implementable acceleration approach for Thompson sampling.
  • To adapt the method for batch allocation scenarios and improve patient satisfaction in preference trials.

Main Methods:

  • Development of a novel acceleration approach for Thompson sampling.
  • Evaluation of the approach through simulation studies.
  • Application to the redesign of a patient preference trial.

Main Results:

  • The proposed acceleration approach significantly improves Thompson sampling's performance.
  • Enhanced average total response rates were observed in simulations.
  • The method is applicable to batch allocation and simplifies implementation.

Conclusions:

  • The novel acceleration approach effectively improves Thompson sampling for clinical trial patient allocation.
  • This method offers a practical solution for optimizing treatment assignment and patient satisfaction.
  • The approach is versatile, applicable to both sequential and batch allocation designs.