Epidemiological data often involve clustered or repeated measures, such as patients within hospitals or repeated observations over time. GLMMs handle this clustering by allowing random effects to account for the non-independence within clusters. This improves the model's ability to make accurate inferences about the population, leading to more robust and reliable results.
