Variable Selection Method in Prediction Models: Application in Periodontology

Abstract

The aim of this study, applied in the field of periodontal diseases, was first to analyze the fatty acid levels in two groups of patients and then to propose a method for selecting the most relevant predictors. Two groups of patients, 29 with moderate or severe periodontitis and 27 who served as controls, were clinically examined, and their fatty acids in serum were measured by gas chromatography. The levels of these 12 fatty acids were the variables of the analysis. Logistic regression, together with the area under the receiver operating characteristic (ROC) curves, allowed determining a composite score which led to a subset of the most relevant covariables. The fatty acid levels differed significantly between the 2 groups in multivariate analysis () and the best logistic model was obtained with only 3 predictive variables: arachidonic acid, linoleic acid, and DHA. Fatty acid levels in serum of patients were significantly different according to the presence of moderate or severe periodontitis. By taking into account the comparison of ROC curves, our approach could optimize the choice of variables in multivariate analyses and could better fit it with diagnosis and prognosis of oral diseases in dental research.