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Clinical development focuses on how the drug will interact with the human body and encompasses four key phases of clinical trials, each serving a specific purpose in assessing the safety and effectiveness of new drugs. These phases overlap and build upon one another. Phase I involves a small group of healthy volunteers (typically 20-80 individuals) or, in cases where significant toxicity is expected, patients with the targeted disease, such as cancer or AIDS. The volunteers are tested for...
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A study design is a set of techniques that allow a researcher to collect and analyze data from different variables defined for a specific research problem. Statistics is commonly for effective study design and more robust experiments,
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Noncompartmental analyses offer an alternative method for describing drug pharmacokinetics without relying on a specific compartmental model. In this approach, the drug's pharmacokinetics are assumed to be linear, with the terminal phase log-linear. This assumption allows for simplified analysis and interpretation of the drug's behavior in the body.
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Incorporating data from multiple ongoing trials for Bayesian two-stage phase II single-arm studies.

Susan Halabi1,2, Taehwa Choi1,3, Elizabeth Garrett-Mayer4

  • 1Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, USA.

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|August 21, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a Bayesian approach to combine data from multiple oncology basket trials, improving recruitment and analysis for rare genomic alterations. This method enhances decision-making in clinical trials with slow patient enrollment.

Keywords:
Basket trialsSimon-two stage designclinical trialshierarchical Bayesian modelrandom effect

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Area of Science:

  • Oncology
  • Clinical Trials
  • Biostatistics

Background:

  • Basket designs are increasingly used in precision medicine oncology clinical trials.
  • The American Society for Clinical Oncology Targeted Agent and Profiling Utilization Registry (TAPUR) study exemplifies a successful basket trial matching patients to targeted therapies based on genomic profiles.
  • Recruiting patients with rare genomic alterations in these trials remains a significant challenge.

Purpose of the Study:

  • To introduce and evaluate a Bayesian approach for integrating data from independent, ongoing basket trials with similar aims.
  • To enhance interim decisions and final analyses in clinical trials.
  • To reduce the number of patients required for treatment evaluation.

Main Methods:

  • A Bayesian two-stage phase II single-arm trial design was developed for rare cancers.
  • The approach utilizes a hierarchical Bayesian random effects model to incorporate data from concurrent trials.
  • The proposed method was compared against the standard Simon two-stage design using extensive numerical simulations and applied to real-world data.

Main Results:

  • Simulation results indicate that the Bayesian approach demonstrates favorable operating characteristics in rare cancer populations.
  • The proposed method performs effectively across various scenarios, including those with fixed and variable numbers of contributing trials.
  • The approach shows promise for improving trial efficiency and decision-making.

Conclusions:

  • The Bayesian two-stage approach successfully integrates data from multiple basket trials, improving the recruitment and analysis of patients with rare genomic alterations.
  • This method optimizes the timing of interim decision-making and final analyses.
  • It offers a valuable strategy for clinical trials facing challenges with slow accrual rates, particularly in rare oncology indications.