covariates

Covariates are variables that are potentially predictive of an outcome and are included in statistical analyses to account for their influence. They are not the primary variable of interest but are controlled for to improve accuracy, reduce variance, or adjust for confounding.

In experimental settings, covariates often include baseline characteristics such as age, sex, or health status. Randomization aims to balance covariates across groups, but researchers may also adjust for covariates in analysis to increase precision or to account for any residual imbalance. In observational studies, covariates are used to control for confounding when estimating the effect of an exposure or treatment. Common adjustment methods include multiple regression, analysis of covariance (ANCOVA), and propensity score techniques that balance covariate distributions between groups.

Covariates can be categorized as fixed or time-varying. Fixed covariates are measured once and treated as constant for the analysis, while time-varying covariates may change during the study and can be incorporated into models that accommodate longitudinal data. In survival analysis and longitudinal models, covariates can be baseline characteristics or time-dependent factors that influence the hazard or trajectory of the outcome.

Interpretation of model results depends on covariates. The coefficient for a covariate represents its association with the outcome after adjusting for other variables in the model and the main exposure. Including covariates can change the estimated effect of the primary variable, particularly if the covariate is a confounder or a mediator.

Limitations include the risk of overfitting with too many covariates, multicollinearity, measurement error, and missing data. Careful selection and measurement of covariates are essential to credible conclusions.