Generalized Linear Mixed Models for Binary Data: Are Matching Results from Penalized Quasi-Likelihood and Numerical Integration Less Biased?

BACKGROUND: Over time, adaptive Gaussian Hermite quadrature (QUAD) has become the preferred method for estimating generalized linear mixed models with binary outcomes. However, penalized quasi-likelihood (PQL) is still used frequently. In this work, we systematically evaluated whether matching resul...

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Detalles Bibliográficos
Autores principales: Benedetti, Andrea, Platt, Robert, Atherton, Juli
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2014
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3886992/
https://www.ncbi.nlm.nih.gov/pubmed/24416249
http://dx.doi.org/10.1371/journal.pone.0084601

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3886992/
https://www.ncbi.nlm.nih.gov/pubmed/24416249
http://dx.doi.org/10.1371/journal.pone.0084601

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