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Sample size determination in clinical trials with multiple co-primary binary endpoints.

Takashi Sozu1, Tomoyuki Sugimoto, Toshimitsu Hamasaki

  • 1The Center for Advanced Medical Engineering and Informatics, Osaka University, 2-2 Yamadaoka, Suita, Osaka 565-0871, Japan. sozu.takashi.4s@kyoto-u.ac.jp

Statistics in Medicine
|August 6, 2010
PubMed
Summary
This summary is machine-generated.

Calculating sample size for clinical trials with multiple binary endpoints is complex. This study provides formulas and methods to determine adequate sample size, showing higher endpoint correlation reduces required sample size.

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Statistical Power Analysis

Background:

  • Clinical trials frequently utilize multiple co-primary efficacy endpoints.
  • Determining appropriate sample size for correlated multiple endpoints presents a significant challenge.

Purpose of the Study:

  • To provide fundamental formulas for calculating statistical power and sample size for multiple binary primary endpoints.
  • To evaluate different statistical methods for sample size determination in multi-endpoint trials.

Main Methods:

  • Development of formulae for power and sample size calculation for binary endpoints.
  • Evaluation of five methods: asymptotic normal (with/without continuity correction), arcsine (with/without continuity correction), and Fisher's exact test.
  • Analysis based on three measures of association between primary endpoints.

Main Results:

  • The required sample size decreases as the association measure between endpoints increases, particularly with high positive correlation.
  • This effect is more pronounced when effect sizes across endpoints are similar.
  • The relationship between association and sample size is weaker when effect sizes differ.

Conclusions:

  • The study offers practical tools for sample size calculation in multi-endpoint clinical trials.
  • Understanding endpoint correlation is crucial for efficient trial design and resource allocation.
  • The findings aid researchers in optimizing sample size determination for complex trial designs.