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BPED: A Bayesian basket design for pediatric trials with external data.

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Summary

This study introduces a Bayesian basket design for pediatric trials using external data (BPED). BPED enhances statistical power by borrowing information across cancer types and external sources, improving treatment effect detection in pediatric oncology.

Keywords:
Basket trialsBayesian adaptive designborrow informationpediatricsubgroups

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Pediatric Oncology

Background:

  • Basket trials evaluate one treatment across multiple indications simultaneously.
  • Pediatric studies face challenges with limited patient accrual, leading to low statistical power in basket designs.

Purpose of the Study:

  • To propose a novel Bayesian basket design for pediatric trials incorporating external data (BPED).
  • To enhance the efficiency and statistical power of pediatric basket trials through dual-information borrowing.

Main Methods:

  • BPED utilizes dual-information borrowing: from external data to pediatric trials and between cancer types within the trial.
  • The design accommodates heterogeneous treatment effects by classifying cancer types into sensitive or insensitive latent subgroups.
  • Subgroup membership is adaptively updated using accumulated pediatric and external data for go/no-go decisions.

Main Results:

  • BPED demonstrates higher power for detecting treatment effects in sensitive cancer types compared to existing designs.
  • The design maintains a desirable type I error rate for insensitive cancer types.
  • Simulation studies validate the improved performance of BPED.

Conclusions:

  • The proposed Bayesian basket design for pediatric trials with external data (BPED) effectively addresses low accrual challenges.
  • BPED offers a statistically robust approach to optimizing treatment evaluation in pediatric oncology basket trials.
  • This design improves decision-making by adaptively identifying responsive cancer types.