Abstract:
When studying the possible effects of several factors in a given disease, two major
problems arise: (1) confounding, and (2) multiplicity of tests. Frequently, in order
to cope with the problem of confounding factors, models with multiple explanatory
variables are used. However, the correlation structure of the variables may be such
that the corresponding tests have low power: in its extreme form this situation is
coined by the term “multicollinearity”. As the problem of multiplicity is still relevant
in these models, the interpretation of results is, in most cases, very hazardous.
We propose a strategy - based on a tree structure of the variables - which provides
a guide to the interpretation and controls the risk of erroneously rejecting null
hypotheses. The strategy was applied to a study of cervical pain syndrome involving
990 subjects and 17 variables. Age, sex, head trauma, posture at work and psychological
status were all found to be important risk factors.
Key-Words:
Cervical Pain Syndrome - Mathematical Modelling - Multicollinearity - Multiple Tests
- Tree Structure
