Multicollinearity may lead to artificial interaction: an example from a cross sectional study of biomarkers

Collinearity is the situation which arises in multiple regression when some or all of the explanatory variables are so highly correlated with one another that it becomes very difficult, if not impossible, to disentangle their influences and obtain a reasonably precise estimate of their effects. Suppressor variable is one of the extreme situations of collinearity that one variable can substantially increase the multiple correlation when combined with a variable that is only modestly correlated with the response variable. In this study, we describe the process by which we disentangled and discovered multicollinearity and its consequences, namely artificial interaction, using the data from cross-sectional quantification of several biomarkers. We showed how the collinearity between one biomarker (blood lead level) and another (urinary trans, trans-muconic acid) and their interaction (blood lead level* urinary trans, trans-muconic acid) can lead to the observed artificial interaction on the third biomarker (urinary 5-aminolevulinic acid).