Author Information

Julie Couton
Walt Stroup

Abstract

Generalized linear mixed models (GLMMs), regardless of the software used to implement them (R, SAS, etc.), can be formulated as conditional or marginal models and can be computed using pseudo-likelihood, penalized quasi-likelihood, or integral approximation methods. While information exists about the small sample behavior of GLMMs for some cases- notably RCBDs with Binomial or count data- little is known about GLMMs for continuous proportions (e.g. Beta) or time-to-event (e.g. Gamma) data or for more complex designs such as the split-plot. In this presentation we review the major model formulation and estimation options and compare their small sample performance for cases listed above.

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Apr 28th, 8:05 AM

ON THE SMALL SAMPLE BEHAVIOR OF GENERALIZED LINEAR MIXED MODELS WITH COMPLEX EXPERIMENTS

Generalized linear mixed models (GLMMs), regardless of the software used to implement them (R, SAS, etc.), can be formulated as conditional or marginal models and can be computed using pseudo-likelihood, penalized quasi-likelihood, or integral approximation methods. While information exists about the small sample behavior of GLMMs for some cases- notably RCBDs with Binomial or count data- little is known about GLMMs for continuous proportions (e.g. Beta) or time-to-event (e.g. Gamma) data or for more complex designs such as the split-plot. In this presentation we review the major model formulation and estimation options and compare their small sample performance for cases listed above.