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Recent Developments on Linear Mixed Models for Longitudinal Data, Possibly Subject to Dropout

Geert Molenberghs, Center for Statistics, Limburgs Universitaire Centrum; and
Geert Verbeke, Biostatistical Center, Catholic University of Leuven

A general introduction to longitudinal data and the linear mixed model will be presented. The topic will be approached from the modeller's and practitioner's points of view. Apart from classical model building strategies, many of which have been implemented in standard statistical software, a number of flexible extensions and additional tools will be discussed. Based on the linear mixed model setting, incomplete data concepts will be reviewed and the issues arising with incomplete data will be underscored. This will be done in both the selection as well as in the pattern-mixture frameworks. Then, it will be argued that an important way forward is by means of sensitivity analysis. To this end, several tools will be discussed, such as a local influence based approach and a method based on so-called uncertainty regions. The use of pattern-mixture models for sensitivity analysis will be considered. Finally, the impact of incompleteness on efficiency will be assessed. 


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