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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...
| Autores principales: | , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
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Public Library of Science
2014
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| 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 |