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Combining several ordinal measures in clinical studies
Author(s) -
Wittkowski Knut M.,
Lee Edmund,
Nussbaum Rachel,
Chamian Francesca N.,
Krueger James G.
Publication year - 2004
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.1778
Subject(s) - multivariate statistics , ordinal data , computer science , ordinal regression , ordinal scale , multivariate analysis , variable (mathematics) , statistics , parametric statistics , linear model , econometrics , data mining , mathematics , machine learning , mathematical analysis
In medical research, it is rare that a single variable is sufficient to represent all relevant aspects of epidemiological risk, genomic activity, adverse events, or clinical response. Since biological systems tend to be neither linear, nor hierarchical in nature, the assumptions of traditional multivariate statistical methods based on the linear model can often not be justified on theoretical grounds. Establishing concept validity through empirical validation is not only problematic, but also time consuming. This paper proposes the use of u‐statistics for scoring multivariate ordinal data and a family of simple non‐parametric tests for analysis. The scoring method is demonstrated to be applicable to scoring clinical response profiles in the treatment of psoriasis and then to identifying genomic pathways that best correlate with these profiles. Copyright © 2004 John Wiley & Sons, Ltd.