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Related Experiment Video

Updated: Sep 24, 2025

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
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Improving clinical trials using Bayesian adaptive designs: a breast cancer example.

Wei Hong1, Sue-Anne McLachlan2,3, Melissa Moore2

  • 1Department of Medical Oncology, St Vincent's Hospital Melbourne, 41 Victoria Parade, Fitzroy, VIC, 3065, Australia. wei.hong@svha.org.au.

BMC Medical Research Methodology
|May 5, 2022
PubMed
Summary

Bayesian adaptive designs significantly improved power and reduced sample sizes in breast cancer clinical trials. These designs offer a more efficient approach for future oncology trial development.

Keywords:
Bayesian adaptive trialPredictive probability of successTime-to-event

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Area of Science:

  • Clinical Trials
  • Biostatistics
  • Oncology

Background:

  • Breast cancer clinical trials often use traditional designs.
  • Evaluating alternative designs like Bayesian adaptive methods is crucial for efficiency.

Purpose of the Study:

  • To virtually re-execute a breast cancer clinical trial using Bayesian adaptive designs.
  • To compare the performance of adaptive designs against the original trial design.

Main Methods:

  • Retrospective re-execution of the ANZ 9311 randomized controlled trial.
  • Computer simulations to estimate power and sample sizes for various Bayesian adaptive designs.
  • Shortlisting designs based on high power or low average sample size.

Main Results:

  • Ten adaptive designs demonstrated higher power, lower average sample size, and reduced false positive rates.
  • Sample size reductions of up to 37% were observed with adaptive designs prioritizing small sample sizes.
  • Power increased by up to 5.9 percentage points without increasing the type I error rate.

Conclusions:

  • Bayesian adaptive designs offer substantial improvements in power and sample size efficiency.
  • Researchers should consider Bayesian adaptive designs for future oncology trial development.