Sample size calculations for studies with correlated observations

Guanghan Liu, Kung Yee Liang

Research output: Contribution to journalArticlepeer-review


Correlated data occur frequently in biomedical research. Examples include longitudinal studies, family studies, and ophthalmologic studies. In this paper, we present a method to compute sample sizes and statistical powers for studies involving correlated observations. This is a multivariate extension of the work by Self and Mauritsen (1988, Biometrics 44, 79-86), who derived a sample size and power formula for generalized linear models based on the score statistic. For correlated data, we appeal to a statistic based on the generalized estimating equation method (Liang and Zeger, 1986, Biometrika 73, 13-22). We highlight the additional assumptions needed to deal with correlated data. Some special cases that are commonly seen in practice are discussed, followed by simulation studies.

Original languageEnglish (US)
Pages (from-to)937-947
Number of pages11
Issue number3
StatePublished - Sep 1997
Externally publishedYes


  • Correlated data
  • Generalized estimating equation
  • Noncentral chi- square
  • Sample size
  • Statistical power

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)
  • Agricultural and Biological Sciences(all)
  • Applied Mathematics


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