Multivariate Analysis of Data on Migraine Treatment

Abstract

Migraineur constitutes a multidimensional model of health disorder involving a complex combination of genetic, psychological, demographic, enviromental and economic factors. This model provides a framework to describe limitations of an individual functional ability and quality of life, and to aid in the elaboration of more adequate intervention programs for each patient. Our primary objective in this paper is a data-driven profiling of patients. The approach followed consists of examining affinity/dissimilarity between sufferers on the basis of different family of indicators and then aggregating multiple partial matrices, where each matrix expresses a particular notion of the dissimilarity of one patient from another. One important particularity of our method is the notion of multi-dimensional dissimilarity for static and dynamic indicators, without ignoring any portion of data. The partial dissimilarity matrices are assembled in the form of a global matrix, which is used as input of subsequent calculations, such as multidimensional scaling and cluster analysis. Our main contribution is to show how multi-scale, cross-section and longitudinal data from individuals involved in a migraine treatment program may optimally be combined to allow profiling migraine-affected patients.