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.
