A survey off methods for analyzing clustered binary response data

Jane F. Pendergast, Stephen J. Gange, Michael A. Newton, Mary J. Lindstrom, Mari Palta, Marian R. Fisher

Research output: Contribution to journalReview articlepeer-review

199 Scopus citations


A comprehensive survey of regression-type models for clusters of correlated binary outcomes, including longitudinal data, is presented. In particular, we focus on models which can accommodate both between- and within-cluster categorical and continuous covariates. Emphasis is given to motivation of the model specification, interrelationships among models, parameter testing and interpretation, estimation methods (including both likelihood and non-likelihood approaches), computational issues, availability of software and other implementation issues, and to the advantages and disadvantages of the various approaches. Models discussed include naïve and response feature models, conditionally specified models, marginal models, and cluster-specific models. Extensions to ordinal data and relationships to graphical representations of models are also discussed.

Original languageEnglish (US)
Pages (from-to)89-118
Number of pages30
JournalInternational Statistical Review
Issue number1
StatePublished - Apr 1996


  • Correlated binary data
  • Generalized estimating equations
  • Generalized linear models
  • Logistic regression
  • Marginal models
  • Ordinal data
  • Overdispersion
  • Random effects models

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


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