An investigation of penalization and data augmentation to improve convergence of generalized estimating equations for clustered binary outcomes

BACKGROUND: In binary logistic regression data are ‘separable’ if there exists a linear combination of explanatory variables which perfectly predicts the observed outcome, leading to non-existence of some of the maximum likelihood coefficient estimates. A popular solution to obtain finite estimates...

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Detalles Bibliográficos
Autores principales: Geroldinger, Angelika, Blagus, Rok, Ogden, Helen, Heinze, Georg
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9178839/
https://www.ncbi.nlm.nih.gov/pubmed/35681120
http://dx.doi.org/10.1186/s12874-022-01641-6

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9178839/
https://www.ncbi.nlm.nih.gov/pubmed/35681120
http://dx.doi.org/10.1186/s12874-022-01641-6

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