title: [Understanding logistic regression].
abstract: Logistic regression is one of the most common multivariate analysis models utilized in epidemiology. It allows the measurement of the association between the occurrence of an event (qualitative dependent variable) and factors susceptible to influence it (explicative variables). The choice of explicative variables that should be included in the logistic regression model is based on prior knowledge of the disease physiopathology and the statistical association between the variable and the event, as measured by the odds ratio. The main steps for the procedure, the conditions of application, and the essential tools for its interpretation are discussed concisely. We also discuss the importance of the choice of variables that must be included and retained in the regression model in order to avoid the omission of important confounding factors. Finally, by way of illustration, we provide an example from the literature, which should help the reader test his or her knowledge.
authors: Inserm, UMRS 872, équipe 17, 15, rue de l'École-de-Médecine, 75006 Paris, France; Centre de recherche des Cordeliers, université Pierre-et-Marie-Curie Paris-VI, UMRS 872, 15, rue de l'École-de-Médecine, 75006 Paris, France; Université Paris Descartes, UMRS 872, 15, rue de l'École-de-Médecine, 75006 Paris, France. Electronic address: mohamed.elsanharawi@gmail.com.
authors: M
authors: M
authors: El Sanharawi
authors: F
authors: F
authors: Naudet
mesh_terms: D032882:Comprehension; D016021:Epidemiologic Studies; D005902:Glaucoma, Open-Angle / Q000453:epidemiology / Q000209:etiology; D006801:Humans; D016015:Logistic Models; D008962:Models, Theoretical; D012307:Risk Factors
keywords: Adjustment; Ajustement; Analyse multivariée; Confounding factor; Facteur de confusion; Interaction; Logistic regression; Multivariate analysis; Odds ratio; Régression logistique