March 23, 2022

Random effects/mixed effects models shine for multi-level data such as measurements within cities within counties within states. They can also deal with measurements clustered within subjects. There are at least two contexts for the latter: rapidly repeated measurements where elapsed time is not an issue, and serial measurements spaced out over time for which time trends are more likely to be important. An example of the first is a series of tests on a subject over minutes when the subject does not fatigue. An example of the second is a typical longitudinal clinical trial where patient responses are assessed weekly or monthly. For the first setup, random effects are likely to capture the important elements of within-subject correlation. Not so much for the second setup, where serial correlation dominates and time ordering is essential. — Longitudinal Data: Think Serial Correlation First, Random Effects Second