Conditional Linear Mixed Models

Part of the Springer Series in Statistics book series (SSS)

As pointed out by Diggle, Liang and Zeger (1994, Section 1.4) and as shown in the examples so far presented in this book, the main advantage of longitudinal studies, when compared to cross-sectional studies, is that they can distinguish changes over time within individuals (longitudinal effects) from differences among people in their baseline values (cross-sectional effects).

Consider a randomized longitudinal clinical trial, where subjects are first randomly assigned to one out of a set of treatments, and then followed for a certain period of time during which measurements are taken at pre-specified time points. Treatment effects are then completely represented by differences in evolutions over time (i.e., by interactions of treatment with time), whereas the randomization assures that, at least in large trials, the treatment groups are completely comparable at baseline with respect to factors which potentially influence change afterward. Hence, a statistical model for such data does not need a cross-sectional model component.


Clinical Trial Treatment Group Treatment Effect Statistical Model Longitudinal Study 
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© Springer Verlag New York, LLC 2009

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