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Estimation and sample size considerations for clustered binary responses

E W Lee1, N Dubin

  • 1Department of Environmental Medicine, New York University Medical Center, NY 10010-2598.

Statistics in Medicine
|June 30, 1994
PubMed
Summary
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This study introduces a simple method for calculating sample sizes for correlated binary outcomes in clinical trials. The weighted procedure improves estimation accuracy for treatment effects and diagnostic accuracy.

Area of Science:

  • Biostatistics
  • Clinical Trials Methodology
  • Statistical Inference

Background:

  • Existing literature primarily addresses sample size determination for independent responses in clinical trials.
  • Methodologies for dependent outcomes, particularly correlated binary outcomes, are lacking.
  • Accurate sample size calculation is crucial for the validity and power of clinical studies.

Purpose of the Study:

  • To present a straightforward method for calculating sample size in the presence of correlated binary outcomes.
  • To enable accurate estimation of treatment effects and diagnostic accuracy with dependent data.
  • To provide practical recommendations for applying the proposed methodology.

Main Methods:

  • Development of a weighted procedure for sample size calculation.

Related Experiment Videos

  • Application of the method to scenarios involving correlated binary outcomes.
  • Demonstration of the procedure's advantages through simulation studies.

    • Main Results:

    • The proposed weighted procedure offers a simple and effective way to determine sample size for correlated binary outcomes.
    • Simulations confirm the advantages of the weighted procedure in estimation accuracy.
    • The method is applicable to both treatment effect estimation and diagnostic accuracy assessment.

    Conclusions:

    • The developed method addresses a critical gap in sample size determination for dependent outcomes.
    • The weighted procedure is recommended for practical application in clinical trials with correlated binary data.
    • This approach enhances the reliability of statistical power and precision in such studies.