Statistical Modelling 1 (2001), 235–269
A Review on Linear Mixed Models for Longitudinal Data, Possibly Subject
to Dropout
R. Geert Molenberghs
Biostatistics, Limburgs Universitair Centrum,
Universitaire Campus, B-3590 Diepenbeek
and
Geert Verbeke
Biostatistical Centre, Catholic University of Leuven,
U.Z. St.-Rafael, Kapucijnenvoer 35, B-3000 Leuven
Abstracts:
Many approaches are
available for the analysis of continuous longitudinal data. Over the last
couple of decades, a lot of emphasis has been put on the linear mixed model.
The current paper is dedicated to an overview of this approach, with emphasis
on model formulation, interpretation and inference. Advantages as well as
drawbacks are discussed, and guidelines are given for general statistical
practice. Special attention is given to the problem of missing data, i.e., the
case where not all data are present as planned in the original design of the
study.
Keywords:
Longitudinal data, Linear Mixed Models, Random Effects, Missing Data,
Dropout, Selection Model, Pattern Mixture Model.
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