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Bayesian selective response-adaptive design using the historical control.

Mi-Ok Kim1,2, Nusrat Harun1, Chunyan Liu3

  • 1UCSF Helen Diller Family Comprehensive Cancer Center, San Francisco, California, USA.

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|June 15, 2018
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Summary
This summary is machine-generated.

This study introduces a Bayesian clinical trial design that adaptively uses historical control data. This approach reduces sample size and trial duration while minimizing bias, improving efficiency in comparative trials.

Keywords:
Bayesian design with commensurate priorsborrowing on the historical control datadoubly adaptive biased coin designresponse-adaptive design

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Bayesian Statistics

Background:

  • Historical control data can optimize clinical trial efficiency by reducing sample size, cost, and duration.
  • However, overly optimistic use of historical data can introduce bias due to prior-data conflict.

Purpose of the Study:

  • To propose a novel Bayesian clinical trial design that adaptively incorporates historical control data.
  • To selectively adjust treatment allocation ratios based on relative treatment effects observed during the trial.

Main Methods:

  • Developed a Bayesian design integrating historical controls with adaptive treatment allocation.
  • Quantified borrowed information using the effective historical sample size approach.
  • Modified doubly adaptive biased coin design rules within a Bayesian framework with commensurate priors to address prior-data conflict.

Main Results:

  • The proposed design adaptively incorporates historical data, reducing control arm allocation only when intervention superiority is suggested.
  • Trials concluded earlier, aligning with true treatment effects, thus reducing overall cost and duration.
  • Achieved parameter estimates with improved precision (smaller standard errors).

Conclusions:

  • This adaptive Bayesian design effectively leverages historical control data to enhance clinical trial efficiency.
  • It mitigates bias associated with prior-data conflict and optimizes resource allocation.
  • The design offers a robust alternative for comparative clinical trials, particularly in fields like stroke research.