Stratified models are particularly useful in situations where confounding factors might distort the true relationship between variables. For instance, when examining the link between smoking and lung cancer, age could be a confounding variable. Stratifying the data by age can help isolate the effect of smoking on lung cancer within each age group, thereby providing a clearer picture of the association.
