Laplace approximation, penalized quasi-likelihood, and adaptive Gauss–Hermite quadrature for generalized linear mixed models: towards meta-analysis of binary outcome with sparse data

BACKGROUND: In meta-analyses of a binary outcome, double zero events in some studies cause a critical methodology problem. The generalized linear mixed model (GLMM) has been proposed as a valid statistical tool for pooling such data. Three parameter estimation methods, including the Laplace approxim...

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Detalles Bibliográficos
Autores principales: Ju, Ke, Lin, Lifeng, Chu, Haitao, Cheng, Liang-Liang, Xu, Chang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7296731/
https://www.ncbi.nlm.nih.gov/pubmed/32539721
http://dx.doi.org/10.1186/s12874-020-01035-6

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7296731/
https://www.ncbi.nlm.nih.gov/pubmed/32539721
http://dx.doi.org/10.1186/s12874-020-01035-6

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