Abstract
Abstract Linear mixed‐effects (LME) models have become a popular tool for analyzing longitudinal data that arise in areas as diverse as clinical trials, epidemiology, agriculture, economics, and geophysics. The increasing popularity of these models is explained by the flexibility they offer in modeling the within‐subject correlation often present in longitudinal data, by the handling of both balanced and unbalanced data, and by the availability of reliable and efficient software for fitting them. This paper describes a general LME model for longitudinal Gaussian data, presenting the model definition and its underlying assumptions. Maximum likelihood and restricted maximum likelihood estimation methods are discussed, as well as asymptotic inference methods. Available software for fitting and analyzing LME models are briefly reviewed. The model and methods are illustrated through an example from a longitudinal growth study.
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