Research Article Variable Selection Method in Prediction Models: Application in Periodontology Paul Tramini, 1 Jean-Christophe Chazel, 2 Isabelle Calas-Bennasar, 3 Philippe Gibert, 3 and Nicolas Molinari 4 1 Dental Public Health, Faculty of Odontology, University of Montpellier 1, 545 avenue du Pr. JL Viala, 34193 Montpellier Cedex 5, France 2 Restorative Dentistry, Faculty of Odontology, University of Montpellier 1, 545 avenue du Pr. JL Viala, 34193 Montpellier Cedex 5, France 3 Periodontology, Faculty of Odontology, University of Montpellier 1, 545 avenue du Pr. JL Viala, 34193 Montpellier Cedex 5, France 4 Biostatistics, Faculty of Medicine, University of Montpellier 1, 545 avenue du Pr. JL Viala, 34193 Montpellier Cedex 5, France Correspondence should be addressed to Paul Tramini; paul.tramini@orange.fr Received 27 September 2013; Revised 16 December 2013; Accepted 23 December 2013; Published 4 February 2014 Academic Editor: Hideki Ohyama Copyright © 2014 Paul Tramini et al. Tis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Te aim of this study, applied in the feld of periodontal diseases, was frst 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. Te 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. Te fatty acid levels difered signifcantly between the 2 groups in multivariate analysis ( = 0.03) 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 signifcantly diferent 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 ft it with diagnosis and prognosis of oral diseases in dental research. 1. Introduction Periodontal disease is one of the most common causes ofooth loss among adults [1, 2]. It is characterized by chronic infammation, periodontal pocket formation together with alveolar bone, and gum destruction. It is now admitted that bacteria alone are insufcient to cause such diseases and the host’s characteristics are indeed known to be determining as well: heredity, tobacco, systemic diseases, and nutrition [3, 4]. So the aetiological role of systemic factors is difcult to separate from that of local factors [5]. It has been demonstrated that the balance between polyunsaturated and saturated serum fatty acids (FA) played an important role in the bone remodelling of the skeleton [6, 7]. So alveolar bone and periodontal tissues can be afected in the same way. Because some polyunsaturated FA are the precursors of prostaglandins which are mediators of infammation, higher levels are associated with infamed tis- sues [8, 9]. Tus, fatty acid measurements could be correlated to periodontal disease, such as periodontitis and alveolar bone loss. Moreover, in this study, we investigated the FA concentrations in the serum of two groups of patients: with periodontitis and without (or with mild) periodontitis. Te aim of this study, applied in the feld of periodontal diseases, was frst to analyze the FA levels in two groups of patients and then to propose a method for selecting the most relevant pre- dictors among a set of quantitative variables in a prediction model. Tis new approach was developed in order to simplify and optimize the choice of predictors, among the numer- ous potential predictive symptoms and biological analyses already described in periodontal research. Hindawi Publishing Corporation Journal of Oral Diseases Volume 2014, Article ID 823530, 7 pages http://dx.doi.org/10.1155/2014/823530