Article

The complexity of rehabilitation outcomes in terms of functioning and
disability leads to the need for dimension reduction in relation to specific
research or clinical problems. Several statistical procedures are at hand. This
article examines whether and to what extent principal component analysis (PCA)
and graphical models differ in the dimension reduction of data based on the
International Classification of Functioning, Disability and Health (ICF).
METHODS: Using a data set of 1048 persons with spinal cord injury from 14
different countries as a case in point, this article compares the solutions in
dimension reduction generated with a graphical model based on least average
shrinkage selection operator (LASSO) regression on one hand and with a PCA on the
other. RESULTS: Some factors extracted with the PCA properly match the clusters
found with the graphical model, while in others less commonality can be found. In
general, overlap ranges from 9 to 100% with 75% on average. Many of the
discovered clusters or factors, i.e. dimensions, are compatible with the ICF
structure, particularly in activity and participation. CONCLUSION: Functioning is
a multidimensional, complex and dynamic outcome. We recommend being careful with
dimension reduction based on statistical procedures alone. Theoretical
considerations and clinical significance should always guide statistics.
Stability of emergent dimensions that are compatible with theoretical or clinical
considerations is a most important point. At least two different statistical
procedures for dimension reduction, for instance PCA and LASSO regression, should
be applied to conservatively select those dimensions that stay stable with both
procedures.

Langue : ANGLAIS