covariabele

Covariabele, in statistics and data analysis, are variables that are possibly predictive of the outcome of interest and are included in a model alongside the primary variables under study. They are used to adjust for differences between observations, reduce unexplained variance, and control for confounding factors.

Common examples include continuous covariates such as age or blood pressure and categorical covariates such as sex or treatment group. Covariates can be time-invariant or time-varying and are typically included as predictors in regression, analysis of covariance (ANCOVA), and related models. The coefficients associated with covariates describe their association with the outcome after accounting for other variables in the model.

In design and analysis, covariates are often measured before treatment or observation to improve precision and power. In randomized trials, adjusting for baseline covariates can tighten estimates without altering the randomization. In observational studies, covariate adjustment helps address confounding but cannot fully eliminate bias from non-random assignment. Careful selection and pre-specification of covariates are important to avoid overfitting and multicollinearity; including too many or inappropriate covariates can distort results.

A covariate is distinct from the main variable of interest and from a mediator. A confounder is a covariate that is associated with both exposure and outcome; properly adjusting for confounders is essential for valid inference. Note that covariate is not to be confused with covariance, which is a measure of joint variability between two variables. In Dutch, covariabelen is the plural form of covariabele.