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Bayesian local exchangeability design for phase II basket trials.

Yilin Liu1, Michael Kane1, Denise Esserman1

  • 1Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut, USA.

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|July 1, 2022
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Summary
This summary is machine-generated.

This study introduces a novel information borrowing strategy for phase II basket trials. The local-MEM framework enhances trial power by borrowing information only between similar disease types, improving efficiency and statistical power.

Keywords:
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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Pharmaceutical Research

Background:

  • Phase II basket trials are crucial for evaluating multiple cancer types with a single investigational drug.
  • Traditional designs may lack statistical power or efficiency when dealing with heterogeneity across disease types.
  • Existing methods for information sharing in basket trials have limitations in flexibility and computational efficiency.

Purpose of the Study:

  • To propose a novel information borrowing strategy for phase II basket trials using a local multisource exchangeability framework (local-MEM).
  • To develop a computationally efficient two-stage design for phase II basket trials that adaptively borrows information based on response rate similarity between disease baskets.
  • To evaluate the performance of the proposed local-MEM design against existing Bayesian methods and Simon's two-stage design.

Main Methods:

  • Development of the local-MEM framework, allowing information borrowing only among baskets with similar response rates.
  • Construction of a two-stage phase II basket trial design incorporating the local-MEM strategy.
  • Comparison of the proposed method with competing Bayesian approaches and Simon's two-stage design through extensive simulations.

Main Results:

  • The proposed local-MEM method effectively maintains the family-wise type I error rate at a controlled level.
  • Demonstrated desirable basket-wise power, outperforming Simon's two-stage design across various simulation scenarios.
  • The method offers computational efficiency, enabling explicit derivation of posterior profiles without complex sampling algorithms.

Conclusions:

  • The local-MEM framework provides a statistically sound and computationally efficient approach for phase II basket trial design.
  • This strategy optimizes information borrowing by considering disease-specific response rate similarity, leading to improved trial power.
  • The proposed method represents a significant advancement in the design and monitoring of adaptive clinical trials for targeted therapies.