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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...
| Autores principales: | , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
BioMed Central
2022
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| Materias: | |
| 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 |