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Understanding logistic regression analysis

Logistic regression is used to obtain odds ratio in the presence of more than one explanatory variable. The procedure is quite similar to multiple linear regression, with the exception that the response variable is binomial. The result is the impact of each variable on the odds ratio of the observed...

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Detalles Bibliográficos
Autor principal: Sperandei, Sandro
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Croatian Society of Medical Biochemistry and Laboratory Medicine 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3936971/
https://www.ncbi.nlm.nih.gov/pubmed/24627710
http://dx.doi.org/10.11613/BM.2014.003
Descripción
Sumario:Logistic regression is used to obtain odds ratio in the presence of more than one explanatory variable. The procedure is quite similar to multiple linear regression, with the exception that the response variable is binomial. The result is the impact of each variable on the odds ratio of the observed event of interest. The main advantage is to avoid confounding effects by analyzing the association of all variables together. In this article, we explain the logistic regression procedure using examples to make it as simple as possible. After definition of the technique, the basic interpretation of the results is highlighted and then some special issues are discussed.