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Controlled multi-arm platform design using predictive probability.

Brian P Hobbs1, Nan Chen1, J Jack Lee1

  • 1Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.

Statistical Methods in Medical Research
|January 15, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces a Bayesian statistical framework for randomized multi-arm platform clinical trials. This approach can reduce sample sizes by 40% and improve response rates by 15% compared to traditional methods.

Keywords:
Bayesian analysismulti-arm controlled clinical trial designmultiple comparisonspredictive probabilitysequential design

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Area of Science:

  • Clinical trial design
  • Biostatistics
  • Drug development

Background:

  • Current one-at-a-time clinical trial screening is inefficient, wasting financial and patient resources.
  • Sequential two-arm trials lead to bias, high costs, and delays between trial phases.

Purpose of the Study:

  • To introduce a Bayesian statistical framework for randomized multi-arm screening platforms.
  • To improve efficiency and resource allocation in clinical trials.
  • To address deficiencies in current agent screening processes.

Main Methods:

  • Utilizing Bayesian modeling for a randomized multi-arm platform design.
  • Consolidating inter-study control arms.
  • Allowing mid-trial modifications (dropping poor performers, adding new agents).

Main Results:

  • Platform designs can reduce mean total sample size by up to 40%.
  • Platform designs can boost mean overall response rate by up to 15%.
  • Potential for considerable cost reductions and elimination of inter-trial bias.

Conclusions:

  • Platform designs offer significant efficiency gains over sequential trials.
  • This framework can alleviate bottlenecks and control multiplicity in drug development.
  • Consequential benefits are expected in oncologic settings with high failure rates.