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, limitin

SUBMITTER: Capanu M 

PROVIDER: S-EPMC3866838 | biostudies-literature | 2013 Nov

REPOSITORIES: biostudies-literature