Analysis of Longitudinal Data: Fixed and Random Effects Models

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Longitudinal data is often encountered by researchers, allowing them to use the time dimension to uncover deeper insights than what is possible with a traditional cross-sectional snapshot. But this advantage comes at a cost: the assumption that each observation is independent is broken due to the fact that patients are measured on multiple occasions over time. Failure to account for this feature when analyzing data can result in bias, and longitudinal methods should be used to account for this problem. Two powerful but simple solutions are the fixed and random effects estimators, both of which have recently become more popular in medical research. A key feature of both is that they model the unobserved differences between patients, and can even control for unobserved confounding. This cou...