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Bayesian response-adaptive designs for basket trials.

Steffen Ventz1,2, William T Barry2,3, Giovanni Parmigiani2,4

  • 1University of Rhode Island, Kingston, Rhode Island.

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This study introduces adaptive Bayesian clinical trial designs for oncology master protocols. These novel designs efficiently identify effective treatments for specific patient subpopulations, optimizing drug development.

Keywords:
Adaptive randomizationBayesian hierarchical modelsMaster protocolsUlti-arm clinical trials

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Area of Science:

  • Clinical Trial Design
  • Biostatistics
  • Oncology Research

Background:

  • Master protocols in oncology investigate multiple treatments across diseases.
  • Treatment efficacy often varies within biomarker-defined subpopulations.
  • Adaptive trials offer efficient evaluation of treatments.

Purpose of the Study:

  • Develop a general class of response-adaptive Bayesian designs for master protocols.
  • Provide open-source software for implementing these adaptive designs.
  • Address two key research goals: subpopulation-finding and subpopulation-stratified designs.

Main Methods:

  • Utilize hierarchical models for response-adaptive Bayesian designs.
  • Framework encompasses existing adaptive trials like I-SPY-2 and BATTLE.
  • Simulations based on cancer sequencing projects quantify design performance.

Main Results:

  • Proposed adaptive designs offer potential gains over conventional non-adaptive designs.
  • Demonstrated application to identifying biomarker subpopulations with treatment efficacy.
  • Showcased utility in selecting superior therapies within subpopulations for specific cancer types.

Conclusions:

  • Response-adaptive Bayesian designs provide a flexible and powerful framework for oncology trials.
  • The developed software facilitates the implementation of these advanced adaptive designs.
  • These methods can enhance the efficiency and success rate of drug development in precision oncology.