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Sample size and power calculations with correlated binary data.

W Pan1

  • 1Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN, USA. weip@biostat.umn.edu

Controlled Clinical Trials
|June 1, 2001
PubMed
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This study provides formulas for sample size and power calculations for correlated binary data in clinical trials. These calculations utilize the robust variance estimator within generalized estimating equations (GEE).

Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Longitudinal Data Analysis

Background:

  • Correlated binary data frequently occur in biomedical research, necessitating robust analytical methods.
  • Generalized estimating equations (GEE) offer a flexible approach for analyzing such data, even with a misspecified correlation structure.
  • The use of robust (sandwich) variance estimators in GEE ensures asymptotically valid statistical inference.

Purpose of the Study:

  • To derive explicit formulas for sample size and power calculations tailored for GEE with correlated binary outcomes.
  • To provide practical tools for researchers planning two-arm clinical trials involving correlated binary data.
  • To enhance the efficiency and accuracy of study design in the presence of correlated data.

Main Methods:

Related Experiment Videos

  • Derivation of explicit formulas for sample size and power.
  • Application of generalized estimating equations (GEE) framework.
  • Utilizing the robust variance estimator for statistical inference.

    • Main Results:

    • Development of practical formulas for sample size and power calculations under GEE.
    • Demonstration of the utility of the robust variance estimator in these calculations.
    • Formulas are applicable to various common scenarios in correlated binary data analysis.

    Conclusions:

    • The derived formulas simplify sample size and power calculations for GEE in two-arm clinical trials with correlated binary outcomes.
    • These tools are expected to aid researchers in the planning stages of such studies.
    • The methodology supports robust statistical inference even when the working correlation matrix is misspecified.