Sample size and power calculations with correlated binary data

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Correlated binary data are common in biomedical studies. Such data can be analyzed using Liang and Zeger's generalized estimating equations (GEE) approach. An attractive point of the GEE approach is that one can use a misspecified working correlation matrix, such as the working independence model (i.e., the identity matrix), and draw (asymptotically) valid statistical inference by using the so-called robust or sandwich variance estimator. In this article we derive some explicit formulas for sample size and power calculations under various common situations. The given formulas are based on using the robust variance estimator in GEE. We believe that these formulas will facilitate the practice in planning two-arm clinical trials with correlated binary outcome data.

Original languageEnglish (US)
Pages (from-to)211-227
Number of pages17
JournalControlled Clinical Trials
Issue number3
StatePublished - Jun 21 2001


  • Clinical trials
  • GEE
  • Logistic regression
  • Sandwich estimator
  • Z-test


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