What is the significance of mixed-effects models in longitudinal studies?
Mixed-effects models, also known as hierarchical or multilevel models, are used to analyze data that have a hierarchical structure, such as repeated measures or clustered data. These models account for both fixed effects (population-level effects) and random effects (individual-level variations). Mixed-effects models are crucial for studying changes over time and for dealing with intra-class correlation.
