Confounding by Indication, Confounding Variables, Covariates, and Independent Variables: Knowing What These Terms Mean and When to Use Which Term

The terms independent variables, covariates, confounding variables, and confounding by indication are often imprecisely used in the context of regression. Independent variables are the full set of variables whose influence on the outcome is studied. Covariates are the independent variables that are included not because they are of interest but because their influence on the outcome can be adjusted for, leaving a more precise understanding of how the single remaining independent variable influences the outcome. Confounding variables are variables that are associated with both independent variables and outcomes; so, the relationship identified between independent variables and outcomes may be due to the confounding variable rather than to the independent variable. Potential confounders should be identified, measured, and adjusted for in regression, just as other covariates are. Confounding by indication occurs when the presence of the independent variable is driven by the confounding variable. Confounding by indication is a special kind of confounding; a confounding variable is a special kind of covariate; and a covariate is a special kind of independent variable in regression analysis. These terms and concepts are explained with the help of examples.