Effects of Omitting Non-confounding Predictors From General Relative-Risk Models for Binary Outcomes

BACKGROUND: The effects, in terms of bias and precision, of omitting non-confounding predictive covariates from generalized linear models have been well studied, and it is known that such omission results in attenuation bias but increased precision with logistic regression. However, many epidemiolog...

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Detalles Bibliográficos
Autores principales: Cologne, John, Furukawa, Kyoji, Grant, Eric J., Abbott, Robert D.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Japan Epidemiological Association 2019
Materias:
Original Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6375815/
https://www.ncbi.nlm.nih.gov/pubmed/30101814
http://dx.doi.org/10.2188/jea.JE20170226

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6375815/
https://www.ncbi.nlm.nih.gov/pubmed/30101814
http://dx.doi.org/10.2188/jea.JE20170226

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