Analyzing Repeated Measurements Using Mixed Models

Longitudinal studies often include multiple, repeated measurements of each patient's status or outcome to assess differences in outcomes or in the rate of recovery or decline over time. Repeated measurements from a particular patient are likely to be more similar to each other than measurements from different patients, and this correlation needs to be considered in the analysis of the resulting data. Many common statistical methods, such as linear regression models, should not be used in this situation because those methods assume measurements to be independent of one another. Here, Detry and Ma detail why mixed models are used for repeated measures data.