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Bayesian hierarchical models for adaptive basket trial designs.

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This study offers guidance on selecting priors for adaptive Bayesian basket trials. It addresses heterogeneity across cancer cohorts to improve trial design and understanding.

Keywords:
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Area of Science:

  • Clinical Trials
  • Biostatistics
  • Oncology

Background:

  • Basket trials test single drugs across multiple cancer types with shared genetic targets.
  • Treatment efficacy in basket trials can vary significantly between patient groups (baskets).
  • Bayesian methods are common in basket trial design, requiring shared information across baskets via prior parameters.

Purpose of the Study:

  • To recommend prior selection for scale parameters in adaptive Bayesian basket trials.
  • To investigate methods for handling heterogeneity across cancer cohorts in basket trials.
  • To enhance the understanding of Bayesian basket trial design properties.

Main Methods:

  • Utilized Bayesian hierarchical modeling to recommend priors for scale parameters.
  • Incorporated flexibility for stratum-specific parameters to be exchangeable or nonexchangeable.
  • Employed simulation studies to assess design performance using statistical power and type I error rates.

Main Results:

  • The proposed Bayesian hierarchical model provides a framework for selecting priors in adaptive basket trials.
  • The design accommodates and evaluates heterogeneity across different cancer cohorts.
  • Simulation results demonstrate the performance characteristics of the proposed design.

Conclusions:

  • The study provides valuable recommendations for prior specification in adaptive Bayesian basket trials.
  • The methods developed can help manage and interpret heterogeneity in multi-cohort cancer trials.
  • This research advances the understanding and application of Bayesian approaches in oncology clinical trials.