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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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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
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author Sperandei, Sandro
author_facet Sperandei, Sandro
author_sort Sperandei, Sandro
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description 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.
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spelling pubmed-39369712014-03-13 Understanding logistic regression analysis Sperandei, Sandro Biochem Med (Zagreb) Lessons in Biostatistics 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. Croatian Society of Medical Biochemistry and Laboratory Medicine 2014-02-15 /pmc/articles/PMC3936971/ /pubmed/24627710 http://dx.doi.org/10.11613/BM.2014.003 Text en © Copyright by Croatian Society of Medical Biochemistry and Laboratory Medicine. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc-nd/3.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Lessons in Biostatistics
Sperandei, Sandro
Understanding logistic regression analysis
title Understanding logistic regression analysis
title_full Understanding logistic regression analysis
title_fullStr Understanding logistic regression analysis
title_full_unstemmed Understanding logistic regression analysis
title_short Understanding logistic regression analysis
title_sort understanding logistic regression analysis
topic Lessons in Biostatistics
url 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
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