In epidemiological research, accurate estimation and interpretation of risk factors are paramount. When predictor variables are highly correlated, it can be challenging to isolate the effect of each variable. VIF quantifies how much the variance of a regression coefficient is inflated due to multicollinearity. This allows researchers to identify and address multicollinearity, ensuring more reliable and valid model estimates.
