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Title
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Longitudinal Data Analysis
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Date / Time / Room
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Sponsor
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Type
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08/13/2002
8:00 AM -
4:00 PM
Room: S-Conference Room D
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ASA, Section on Statistics in Epidemiology*
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Other
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Organizer:
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n/a
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Chair:
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n/a
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CE Presenter
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- Center for Statistics, Belgium
- Center for Statistics, Belgium
- Biostatistical Centre, Belgium
- Biostatistical Centre, Belgium
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Description
Based on Verbeke and Molenberghs (Springer, 1997, 2000), a general introduction to longitudinal data and the linear mixed model for continuous responses will be presented. The topic will be approached from the modeller's and practitioner's point of view. Emphasis will be on model formulation, parameter estimation, and hypothesis testing, as well s on the distinction between the random-effects (hierarchical) model and the implied marginal model. Apart from classical model building strategies, many of which have been implemented in the standard statistical software, a number of flexible extensions and additional tools for model diagnosis will be indicated. Illustrations will be given based on the SAS procedure MIXED.
When the response of interest is categorical, the linear mixed model concepts can be extended towards generalized linear mixed models. An alternative approach is the use of generalized estimating equations (GEE). A lot of emphasis will be put on the fact that the regression parameters in both types of models have different interpretations. Advantages and disadvantages of both procedures will be discussed and compared in detail, and illustrations will be based on the SAS procedures GENMOD and MLMIXED. Some other approaches will be sketched briefly.
Finally, when analyzing longitudinal data, one is often confronted with missing observations, i.e., scheduled measurements have not been made, due to a variety of (known or unknown) reasons. It will be shown that, if no appropriate measures are taken, missing data can cause seriously biased results, and interpretational difficulties.
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