covariabel

A covariate is a variable that is possibly predictive of a study outcome and is included in statistical analyses to adjust for its influence or to improve the precision of estimated effects. Covariates can be measured before the intervention (baseline) or during the study, and they can be numeric or categorical.

In experimental design, covariates are used to reduce residual variance and to check whether randomization achieved balance across groups. While randomization aims to distribute covariates evenly, researchers often adjust for important covariates in analysis to account for any imbalance or to increase statistical power. In observational studies, covariates often include potential confounders—variables that are related to both the exposure and the outcome and could bias estimates if not controlled.

In regression and related models, a covariate is a predictor variable included alongside the primary independent variable(s). The coefficient for a covariate represents its association with the outcome after holding other variables constant. Covariates can be continuous (for example, age or baseline measurement) or categorical (for example, sex or treatment site). Researchers may also explore interactions between covariates and the primary variable to assess effect modification.

Common examples include age, sex, baseline disease severity, socioeconomic status, and comorbidity indices. Handling covariates involves decisions about inclusion, potential centering or standardization, and addressing issues such as multicollinearity or missing data. Overuse of covariates or poor measurement can introduce bias or reduce degrees of freedom, so covariate selection is typically guided by prior knowledge and study design. The concept is closely related to, but distinct from, confounding, and is central to methods like ANCOVA and propensity score adjustment.