Assessing the effect of one variable while accounting for the effect of another (confounding) variable. Adjustment for the other variable can be carried out by stratifying the analysis (especially if the variable is categorical) or by statistically estimating the relationship between the variable and the outcome and then subtracting out that effect to study which effects are “left over.” For example, in a non-randomized study comparing the effects of treatments A and B on blood pressure reduction, the patients’ ages may have been used to select the treatment.
It would be advisable in that case to control for the effect of age before estimating the treatment effect. This can be done using a regression model with blood pressure as the dependent variable and treatment and age as the independent variables (controlling for age using subtraction) or crudely and approximately (with some residual confounding) by stratifying by deciles of age and averaging the treatment effects estimated within the deciles. Adjustment results in adjusted odds ratios, adjusted hazard ratios, adjusted slopes, etc.
