Data may exhibit dependencies for many reasons. If a patient’s medical condition is measured across several time points, it seems unlikely that these measurements are totally unrelated. Educational ...

Linear mixed-effects models are frequently used to analyze repeated measures data, because they model flexibly the within-subject correlation often present in this type of data. The most popular ...

Just as PROC GLM is the flagship procedure for fixed-effect linear models, the MIXED procedure is the flagship procedure for random- and mixed-effect linear models. PROC MIXED fits a variety of mixed ...

This section provides an overview of a likelihood-based approach to general linear mixed models. This approach simplifies and unifies many common statistical analyses, including those involving ...

Nonlinear mixed effects models (NLMMs) and self-modeling nonlinear regression (SEMOR) models are often used to fit repeated measures data. They use a common function shared by all subjects to model ...

This course will discuss the concept of random effects, why they are called random effects and how they are incorporated in the framework of mixed models. The primary focus of the course will be to ...