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Related Experiment Videos

Sample size for K 2x2 tables in equivalence studies using Cochran's statistic.

James X Song1, James T Wassell

  • 1Global Biometry, Bayer Pharmaceuticals Corporation, West Haven, Connecticut, USA. james.song.b@bayer.com

Controlled Clinical Trials
|July 17, 2003
PubMed
Summary

This study introduces a new sample size formula for Cochran's test in equivalence studies, reducing the number of subjects needed by utilizing stratum-specific success rates. This method is more efficient, especially when success rates vary across clinical trial centers.

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Area of Science:

  • Biostatistics
  • Clinical Trials Methodology
  • Statistical Inference

Background:

  • Equivalence studies aim to demonstrate comparable efficacy between a new and standard therapy.
  • Stratification is crucial in clinical trials to account for variations in success rates across different centers.
  • Cochran's test is employed for comparing proportions within strata, but sample size calculations can be inefficient.

Purpose of the Study:

  • To develop a novel sample size formula for Cochran's test in equivalence studies.
  • To enhance efficiency by incorporating stratum-specific success rate information.
  • To reduce the number of subjects required compared to existing methods.

Main Methods:

  • Derivation of a new sample size formula for comparing two independent binomial proportions within strata.

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  • Implementation strategies for large numbers of centers with unknown exact success rates.
  • Simulation studies to assess the impact of success rate variability on test power.

    • Main Results:

    • The new formula requires fewer subjects than methods ignoring stratification.
    • The efficiency gains increase with greater variability in success rates among centers.
    • The intracluster correlation coefficient effectively measures success rate variability.

    Conclusions:

    • The proposed sample size formula offers significant subject savings in stratified equivalence studies.
    • Accounting for stratum-specific success rates improves statistical power and efficiency.
    • This method is particularly beneficial when inter-center success rate variability is high.