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Sample Size Considerations in Clinical Trials when Comparing Two Interventions using Multiple Co-Primary Binary

Yuki Ando1, Toshimitsu Hamasaki2, Scott R Evans3

  • 1Biostatistics Group, Center for Product Evaluation, Pharmaceuticals and Medical Devices Agency, Japan ; Department of Mathematical Health Science, Osaka University Graduate School of Medicine, Japan.

Statistics in Biopharmaceutical Research
|July 14, 2015
PubMed
Summary
This summary is machine-generated.

This study addresses limited methods for clinical trials with multiple binary endpoints. It provides power and sample size calculations for correlated binary endpoints to ensure robust intervention comparisons.

Keywords:
Co-primary endpointsConjunctive powerGroup-sequential designsMonte-Carlo simulationNormal approximationType I error

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Medical Product Development

Background:

  • Multiple primary endpoints offer comprehensive intervention characterization and comparisons in clinical trials.
  • Common in oncology, infectious disease, and cardiovascular disease, multiple endpoints are crucial for evaluating new interventions.
  • Existing methodologies for continuous co-primary endpoints are well-developed, but limited for binary endpoints.

Purpose of the Study:

  • To describe power and sample size determination for clinical trials featuring multiple correlated binary endpoints.
  • To evaluate the superiority of a test intervention against a control intervention across all relative risks.
  • To extend methods for interim monitoring using group-sequential approaches.

Main Methods:

  • Discusses normal approximation methods for power and sample size calculations.
  • Evaluates the impact of endpoint correlations on sample size, power, and Type I error.
  • Presents a simple, conservative procedure for sample size calculation.

Main Results:

  • Provides methodologies for power and sample size determination for multiple correlated binary endpoints.
  • Demonstrates how correlations influence required sample size, power, and Type I error rates.
  • Offers a framework for sample size calculation and interim analysis in such trials.

Conclusions:

  • The study addresses a critical gap in statistical methodologies for clinical trials with multiple binary endpoints.
  • The developed methods facilitate more accurate and efficient clinical trial design and analysis.
  • The findings support robust evaluation of interventions using multiple correlated binary outcomes.