A review on linear mixed models for longitudinal data, possibly subject to dropout

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....

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Bibliographic Details
Published inStatistical modelling Vol. 1; no. 4; pp. 235 - 269
Main Authors Molenberghs, Geert, Verbeke, Geert
Format Journal Article
LanguageEnglish
Published Thousand Oaks, CA SAGE Publications 01.12.2001
Subjects
pattern mixture model
random effects
selection model
linear mixed models
dropout
missing data
longitudinal data
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ISSN1471-082X
1477-0342
DOI10.1177/1471082X0100100402

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Summary: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.
ISSN:1471-082X
1477-0342
DOI:10.1177/1471082X0100100402