Marginalized binary mixed-effects models with covariate-dependent random effects and likelihood inference

Zengri Wang, Thomas A. Louis

Research output: Contribution to journalArticlepeer-review

21 Scopus citations


Marginal models and conditional mixed-effects models are commonly used for clustered binary data. However, regression parameters and predictions in nonlinear mixed-effects models usually do not have a direct marginal interpretation, because the conditional functional form does not carry over to the margin. Because both marginal and conditional inferences are of interest, a unified approach is attractive. To this end, we investigate a parameterization of generalized linear mixed models with a structured random-intercept distribution that matches the conditional and marginal shapes. We model the marginal mean of response distribution and select the distribution of the random intercept to produce the match and also to model covariate-dependent random effects. We discuss the relation between this approach and some existing models and compare the approaches on two datasets.

Original languageEnglish (US)
Pages (from-to)884-891
Number of pages8
Issue number4
StatePublished - Dec 2004
Externally publishedYes


  • Bridge distribution
  • Clustered data
  • Gaussian-Hermite quadrature
  • Marginal model
  • Random-effects model

ASJC Scopus subject areas

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


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