title: [Clinical research XIX. From clinical judgment to analysis of covariance].
abstract: The analysis of covariance (ANCOVA) is based on the general linear models. This technique involves a regression model, often multiple, in which the outcome is presented as a continuous variable, the independent variables are qualitative or are introduced into the model as dummy or dichotomous variables, and factors for which adjustment is required (covariates) can be in any measurement level (i.e. nominal, ordinal or continuous). The maneuvers can be entered into the model as 1) fixed effects, or 2) random effects. The difference between fixed effects and random effects depends on the type of information we want from the analysis of the effects. ANCOVA effect separates the independent variables from the effect of co-variables, i.e., corrects the dependent variable eliminating the influence of covariates, given that these variables change in conjunction with maneuvers or treatments, affecting the outcome variable. ANCOVA should be done only if it meets three assumptions: 1) the relationship between the covariate and the outcome is linear, 2) there is homogeneity of slopes, and 3) the covariate and the independent variable are independent from each other.
authors.affiliation: Centro de Adiestramiento en Investigación Clínica (CAIC), Coordinación de Investigación en Salud, Centro Médico Nacional Siglo XXI, Instituto Mexicano del Seguro Social. Distrito Federal, México. jotalaverap@uaemex.mx.
authors.forename: Marcela
authors.initials: M
authors.lastname: Pérez-Rodríguez
authors.forename: Lino
authors.initials: L
authors.lastname: Palacios-Cruz
authors.forename: Jorge
authors.initials: J
authors.lastname: Moreno
authors.forename: Rodolfo
authors.initials: R
authors.lastname: Rivas-Ruiz
authors.forename: Juan O
authors.initials: JO
authors.lastname: Talavera
mesh_terms: D000704:Analysis of Variance; D035843:Biomedical Research; D003627:Data Interpretation, Statistical; D016014:Linear Models; D012107:Research Design
keywords: Analysis of covariance; Factor analysis; Statistical