Generalized Linear Mixed Models

Abstract Generalized linear mixed models (GLMMs) are an extension of the class of generalized linear models in which random effects are added to the linear predictor. This allows the modeling of correlated, possibly nonnormally distributed data with flexible accommodation of covariates. We describe this class of models and the inferences possible and contrast them with marginal modeling techniques such as generalized estimating equations.