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Related Experiment Video

Updated: Oct 3, 2025

Cutoff Value of Phase Angle by Bioelectrical Impedance Analysis at Admission as a Prognostic Factor in Patients with Acute Heart Failure
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Bayesian two-stage design for phase II oncology trials with binary endpoint.

Lichang Chen1,2, Jianhong Pan1, Yanpeng Wu1

  • 1Department of Biostatistics, School of Public Health, Southern Medical University, Guangzhou, China.

Statistics in Medicine
|February 18, 2022
PubMed
Summary

This study introduces a novel Bayesian design for phase II oncology trials, improving early stopping decisions for futility and efficacy. The new approach ensures response rates align with pre-specified posterior probability levels, offering an alternative to frequentist methods.

Keywords:
BayesianSimon's two-stage designbinary endpointposterior probabilitypredictive probability

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Area of Science:

  • Oncology
  • Biostatistics
  • Clinical Trial Design

Background:

  • Phase II oncology trials commonly use two-stage designs for early stopping based on frequentist statistics.
  • Frequentist designs may not guarantee high posterior probabilities for clinically relevant response rates from a Bayesian viewpoint.

Purpose of the Study:

  • To propose a novel Bayesian two-stage design for phase II oncology trials.
  • To enable early termination for both futility and efficacy while ensuring high posterior probabilities.

Main Methods:

  • Incorporated clinically uninteresting and interesting response rates, prior response rate distributions, minimum posterior threshold probabilities, and highest posterior density intervals.
  • Defined a feasible design maximizing the total effective predictive probability.
  • Studied design properties and applied it to a real oncology trial.

Main Results:

  • The proposed Bayesian design effectively allows for early stopping for futility and efficacy.
  • Ensured observed response rates fell within pre-specified posterior probability levels.
  • Demonstrated the design's applicability in an example oncology trial.

Conclusions:

  • The novel Bayesian design offers a robust alternative to traditional frequentist two-stage trials in oncology.
  • This approach enhances decision-making by incorporating Bayesian principles for probability assessment.
  • Provides a framework for more reliable early stopping in single-arm, two-stage oncology trials.