One of the primary reasons for using multivariate models in epidemiology is to control for confounding variables. Confounders are variables that can distort the true relationship between the exposure and the outcome. For instance, when studying the link between smoking and lung cancer, it is important to adjust for age, as age is a potential confounder. Multivariate models allow researchers to isolate the effect of the primary exposure by adjusting for these additional variables.
