Table of Contents
Journal of Oral Diseases
Volume 2014 (2014), Article ID 823530, 7 pages
http://dx.doi.org/10.1155/2014/823530
Research Article

Variable Selection Method in Prediction Models: Application in Periodontology

1Dental Public Health, Faculty of Odontology, University of Montpellier 1, 545 avenue du Pr. JL Viala, 34193 Montpellier Cedex 5, France
2Restorative Dentistry, Faculty of Odontology, University of Montpellier 1, 545 avenue du Pr. JL Viala, 34193 Montpellier Cedex 5, France
3Periodontology, Faculty of Odontology, University of Montpellier 1, 545 avenue du Pr. JL Viala, 34193 Montpellier Cedex 5, France
4Biostatistics, Faculty of Medicine, University of Montpellier 1, 545 avenue du Pr. JL Viala, 34193 Montpellier Cedex 5, France

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. This 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.

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.