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PMED: Optimal Bayesian Platform Trial Design with Multiple Endpoints.

Tian He1, Rachael Liu2, Meizi Liu2

  • 1Department of Biostatistics and Health Data Science, Indiana University, Indianapolis, Indiana, USA.

Journal of Biopharmaceutical Statistics
|August 24, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces an optimal Bayesian platform trial design (PMED) for oncology, integrating multiple endpoints to assess benefit-risk profiles. It enhances treatment and indication selection while managing risks and improving decision-making in early-phase drug development.

Keywords:
Bayesian hierarchical modeladaptive trial designhierarchical testingmultiple endpointsplatform design

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Area of Science:

  • Clinical Trials
  • Biostatistics
  • Oncology Drug Development

Background:

  • Early-phase oncology trials focus on indication and dose selection, crucial for success.
  • Master protocols (basket, umbrella, platform trials) are popular but often lack safety endpoint integration.
  • Existing designs may not offer a comprehensive framework for optimal treatment selection, risking future development.

Purpose of the Study:

  • To propose an optimal Bayesian platform trial design with multiple endpoints (PMED) for characterizing the overall benefit-risk profile in oncology.
  • To extend the design for treatment and indication selection within and across arms, including futility monitoring.
  • To introduce dynamic borrowing across arms for enhanced estimation efficiency and accuracy.

Main Methods:

  • Developed an optimal Bayesian platform trial design (PMED) incorporating multiple endpoints.
  • Integrated treatment and indication selection with continuous interim analyses for futility.
  • Employed dynamic borrowing across arms and a hierarchical hypothesis structure.
  • Utilized simulation studies to evaluate design performance.

Main Results:

  • The proposed PMED design effectively characterizes the overall benefit-risk profile.
  • The design allows for optimal treatment and indication selection within and across arms.
  • Dynamic borrowing enhances estimation efficiency and accuracy.
  • Simulation studies demonstrated the robustness, superb power, and controlled family-wise error rate of PMED.

Conclusions:

  • PMED provides a comprehensive quantitative framework for optimal treatment selection in oncology drug development.
  • The design addresses the limitations of existing protocols by integrating efficacy and safety endpoints.
  • PMED improves decision-making, increases trial efficiency, and reduces the risk of future development failures.