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The aim of this paper was to describe a general framework of multiway multidimensional analysis as
a tool to analyse pharmaco-EEG data. The multiway method chosen enables data reduction of the
complex structure of the data, providing a quantified hierarchy of effects (sets of linear
components on each mode). The component can then be used for testing the drug
effect with any cross-over analysis. Even with simple preprocessing the method performed well in
extracting main variability features of the data but also revealed outliers.
The existence of outliers seems to be an important aspect of this kind of data because of a strong
subject variability of EEG recordings associated associated with drug intake. Without discussing
sample size required for this type of analysis, (which would need to be fully addressed) this puts
into question the use of subjects as a mode (or combined with doses) in the analysis and enforce
to use summaries across subjects. Centring , scaling, and interactions removal were used to try to
avoid outliers. Another approach was to represent doses by a summary measure across subjects
(mean, median, trimean) and do the analysis with this mode. A larger sample would reduce outlier
problem but would also improve estimation of the summaries. Analysing robust summaries may be
interesting when comparing or classifying different drugs if the same design was used but not
necessarily on the same subjects. Supplementary points technique can confirm and add more
information on dispersion of the evolution of the dose-time profiles.
The method described here can involve the use of metrics as in generalised multidimensional
analysis, e.g. discriminant analysis. Note that the problem in pharmaco-EEG analysis is not
necessarily a discriminant one as in the first place the neuro-pharmacist wants to identify
effects of the drug and only secondly dose pattern effects. Correspondence Analysis on modes
was introduced as a particular PTA-modes of a uple. This method seem very well suited
to pharmaco-EEG data as conditional independence can be analysed and quantified. Complete
independence, two way interactions, three way interactions take part of the same analysis, and are
in turn also decomposed as sum of Principal Tensors. Analysing the links between pharmaco-dynamic
variables and pharmaco-kinetic variables has not been investigated in this paper, but implementing
for example inter-battery analysis ideas ([21]) with PTA-modes seems
straightforward. An expending literature about multiway PLS (Partial Least Squares) is worth
reading for this purpose.
Subsections
Next: Acknowledgments
Up: tr00dl2
Previous: FCA-modes for pharmaco-EEG
Didier Leibovici
2001-09-04