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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...
| Autores principales: | , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Japan Epidemiological Association
2019
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| Materias: | |
| 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 |