title: Logistic regression models.
abstract: In the health sciences it is quite common to carry out studies designed to determine the influence of one or more variables upon a given response variable. When this response variable is numerical, simple or multiple regression techniques are used, depending on the case. If the response variable is a qualitative variable (dichotomic or polychotomic), as for example the presence or absence of a disease, linear regression methodology is not applicable, and simple or multinomial logistic regression is used, as applicable.
authors.affiliation: Fundación para la Investigación Biosanitaria de Andalucía Oriental (FIBAO), Complejo Hospitalario de Jaén, Spain.
authors.forename: S
authors.initials: S
authors.lastname: Domínguez-Almendros
authors.forename: N
authors.initials: N
authors.lastname: Benítez-Parejo
authors.forename: A R
authors.initials: AR
authors.lastname: Gonzalez-Ramirez
mesh_terms: D000818:Animals; D015986:Confounding Factors, Epidemiologic; D015340:Epidemiologic Research Design; D006801:Humans; D016013:Likelihood Functions; D016015:Logistic Models; D017711:Nonlinear Dynamics; D011157:Population Dynamics; D012307:Risk Factors; D012984:Software