An assessment of estimation methods for generalized linear mixed models with binary outcomes Journal Article


Authors: Capanu, M.; Gonen, M.; Begg, C. B.
Article Title: An assessment of estimation methods for generalized linear mixed models with binary outcomes
Abstract: Two main classes of methodology have been developed for addressing the analytical intractability of generalized linear mixed models: likelihood-based methods and Bayesian methods. Likelihood-based methods such as the penalized quasi-likelihood approach have been shown to produce biased estimates especially for binary clustered data with small clusters sizes. More recent methods using adaptive Gaussian quadrature perform well but can be overwhelmed by problems with large numbers of random effects, and efficient algorithms to better handle these situations have not yet been integrated in standard statistical packages. Bayesian methods, although they have good frequentist properties when the model is correct, are known to be computationally intensive and also require specialized code, limiting their use in practice. In this article, we introduce a modification of the hybrid approach of Capanu and Begg, 2011, Biometrics 67, 371-380, as a bridge between the likelihood-based and Bayesian approaches by employing Bayesian estimation for the variance components followed by Laplacian estimation for the regression coefficients. We investigate its performance as well as that of several likelihood-based methods in the setting of generalized linear mixed models with binary outcomes. We apply the methods to three datasets and conduct simulations to illustrate their properties. Simulation results indicate that for moderate to large numbers of observations per random effect, adaptive Gaussian quadrature and the Laplacian approximation are very accurate, with adaptive Gaussian quadrature preferable as the number of observations per random effect increases. The hybrid approach is overall similar to the Laplace method, and it can be superior for data with very sparse random effects. © 2013 John Wiley & Sons, Ltd.
Keywords: bayesian; pseudo-likelihood; laplace; binary clustered data; generalized linear mixed models; pql
Journal Title: Statistics in Medicine
Volume: 32
Issue: 26
ISSN: 0277-6715
Publisher: John Wiley & Sons  
Date Published: 2013-11-20
Start Page: 4550
End Page: 4566
Language: English
DOI: 10.1002/sim.5866
PROVIDER: scopus
PUBMED: 23839712
PMCID: PMC3866838
DOI/URL:
Notes: --- - "Export Date: 1 November 2013" - "CODEN: SMEDD" - "Source: Scopus"
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  1. Colin B Begg
    306 Begg
  2. Mithat Gonen
    1030 Gonen
  3. Marinela Capanu
    386 Capanu