Multilevel analysis

Multilevel analysis, also called mixed model analysis, deals with data where not all observations are independent. This includes data generated through longitudinal studies, where the same individual is measured repeatedly over time, grouped data, where individuals within a group are more similar to one another compared to people outside the group, or laboratory tests where the experiments were carried out in separate batches, and where batch effects cannot be excluded.

Starting from ANOVA models with random factor levels, the concepts of mixed models are introduced and the basics about inference in random-effects models will be explained. Afterwards, the mixed ANOVA model is extended to general linear mixed models for continuous data. Omitting as much theoretical details as possible, sufficient background will be given such that practicing statisticians can apply mixed models in a variety of contexts, know how to use up-to-date software, and are able to correctly interpret generated outputs. Many applications, taken from various disciplines, will be discussed.

At the end of this module, participants should be able to:

· recognize clustered data

· know what statistical analysis methods should and be used to account for the clustered nature

· formulate, fit and interpret basic mixed or multilevel models for the analysis of clustered data, repeated measures or longitudinal data

· The module is taught in 3 sessions, all of them are foreseen as hands-on sessions using R software.

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