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A confidence function-based posterior probability design for phase II cancer trials.

Minghua Shan1

  • 1Bayer U.S. LLC Pharmaceuticals, Whippany, New Jersey, USA.

Pharmaceutical Statistics
|December 18, 2020
PubMed
Summary

New Bayesian oncology trial designs offer continual monitoring for objective response rate (ORR) decisions. These methods provide clear stopping rules and optimize sample size, improving phase II trial efficiency.

Keywords:
Bayesian posterior probabilitycontinual monitoringphase II clinical trialsample sizetwo-stage design

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Area of Science:

  • Clinical Trials
  • Biostatistics
  • Oncology Drug Development

Background:

  • Single-arm, multi-stage study designs are standard in phase II oncology trials for binary outcomes like tumor response.
  • Existing two-outcome (e.g., Simon's) and three-outcome designs aim to reject null or alternative hypotheses regarding objective response rate (ORR).
  • Three-outcome designs offer a "gray zone" to reduce study size by not rejecting either hypothesis.

Purpose of the Study:

  • To introduce novel two- and three-outcome phase II oncology trial designs incorporating continual Bayesian monitoring.
  • To ensure these new designs meet frequentist error rate specifications (Type I and II errors).
  • To optimize designs by minimizing loss functions, such as average sample size, under the null hypothesis.

Main Methods:

  • Development of two- and three-outcome designs utilizing Bayesian posterior probabilities for decision-making.
  • Implementation of futility and/or efficacy boundaries based on confidence functions for interpretable stopping rules.
  • Systematic search within a class of procedures to identify optimal designs minimizing specific loss functions.

Main Results:

  • Proposed designs incorporate continual monitoring with clear, interpretable futility and efficacy boundaries.
  • The Bayesian approach ensures adherence to frequentist Type I and II error rate specifications.
  • Optimized designs demonstrate favorable operating characteristics compared to existing procedures.

Conclusions:

  • The novel Bayesian designs enhance phase II oncology trial efficiency through continual monitoring and optimized stopping rules.
  • These designs offer a robust framework for decision-making in oncology drug development, balancing statistical rigor with practical considerations.
  • The proposed methodology provides a valuable alternative for evaluating objective response rates in clinical trials.