In epidemiological studies, especially case-control studies, matching is often employed to control for confounding factors. For instance, cases (people with the disease) might be matched to controls (people without the disease) based on age, sex, or other variables. Regular logistic regression does not account for this matching, which can lead to biased estimates. Conditional logistic regression corrects for this by focusing on the differences within matched sets.
