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Abstract
The general linear mixed model provides a useful approach for analysing a wide variety
of data structures which practising statisticians often encounter. Two such data structures
which can be problematic to analyse are unbalanced repeated measures data and longitudinal
data. Owing to recent advances in methods and software, the mixed model analysis is
now readily available to data analysts. The model is similar in many respects to ordinary
multiple regression, but because it allows correlation between the observations, it
requires additional work to specify models and to assess goodness-of-fit. The extra
complexity involved is compensated for by the additional flexibility it provides in
model fitting. The purpose of this tutorial is to provide readers with a sufficient
introduction to the theory to understand the method and a more extensive discussion
of model fitting and checking in order to provide guidelines for its use. We provide
two detailed case studies, one a clinical trial with repeated measures and dropouts,
and one an epidemiological survey with longitudinal follow-up.