In epidemiology, data often exhibit hierarchical structures. For example, patient data may be nested within hospitals, or survey responses may be nested within regions. Ignoring these structures can lead to incorrect inferences by underestimating the variability within and between clusters. A random effects model helps to:
1. Account for Within-Group Correlation: By including random effects, the model acknowledges that observations within the same group may be more similar than those from different groups. 2. Improve Estimates: It provides more accurate parameter estimates by appropriately modeling the variability at different levels. 3. Generalize Findings: The results can be generalized to a broader population beyond the specific groups studied.
