title: Understanding logistic regression analysis.
abstract: 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.
authors.affiliation: School of Physical Education and Sports - Federal University of Rio de Janeiro, Rio de Janeiro, Brazil.
authors.forename: Sandro
authors.initials: S
authors.lastname: Sperandei
mesh_terms: D000328:Adult; D000900:Anti-Bacterial Agents / Q000627:therapeutic use*; D004697:Endocarditis, Bacterial / Q000188:drug therapy* / Q000401:mortality; D006801:Humans; D016014:Linear Models; D016015:Logistic Models; D008875:Middle Aged; D016017:Odds Ratio; D013203:Staphylococcal Infections / Q000188:drug therapy* / Q000401:mortality; D013211:Staphylococcus aureus / Q000187:drug effects*; D015996:Survival Rate
chemical_list: D000900:Anti-Bacterial Agents
keywords: logistic regression; odds ratio; regression analysis; variable selection