Assessing Risk Factors for Dental Caries: A Statistical Modeling Approach

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Title: Assessing Risk Factors for Dental Caries: A Statistical Modeling Approach
Authors: Trottini, Mario | Bossù, Maurizio | Corridore, Denise | Ierardo, Gaetano | Luzzi, Valeria | Saccucci, Matteo | Polimeni, Antonella
Research Group/s: Métodos Estadístico-Matemáticos para el Tratamiento de Datos de Observación de la Tierra (MEMOT)
Center, Department or Service: Universidad de Alicante. Departamento de Estadística e Investigación Operativa
Keywords: Caries risk assessment | Risk indicators | Zero inflation | Hurdle models | Model selection | Correction for optimism
Knowledge Area: Estadística e Investigación Operativa
Issue Date: 4-Mar-2015
Publisher: Karger
Citation: Caries Research. 2015, 49(3): 226-235. doi:10.1159/000369831
Abstract: The problem of identifying potential determinants and predictors of dental caries is of key importance in caries research and it has received considerable attention in the scientific literature. From the methodological side, a broad range of statistical models is currently available to analyze dental caries indices (DMFT, dmfs, etc.). These models have been applied in several studies to investigate the impact of different risk factors on the cumulative severity of dental caries experience. However, in most of the cases (i) these studies focus on a very specific subset of risk factors; and (ii) in the statistical modeling only few candidate models are considered and model selection is at best only marginally addressed. As a result, our understanding of the robustness of the statistical inferences with respect to the choice of the model is very limited; the richness of the set of statistical models available for analysis in only marginally exploited; and inferences could be biased due the omission of potentially important confounding variables in the model's specification. In this paper we argue that these limitations can be overcome considering a general class of candidate models and carefully exploring the model space using standard model selection criteria and measures of global fit and predictive performance of the candidate models. Strengths and limitations of the proposed approach are illustrated with a real data set. In our illustration the model space contains more than 2.6 million models, which require inferences to be adjusted for ‘optimism'.
ISSN: 0008-6568 (Print) | 1421-976X (Online)
DOI: 10.1159/000369831
Language: eng
Type: info:eu-repo/semantics/article
Rights: © 2015 S. Karger AG, Basel
Peer Review: si
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Appears in Collections:INV - SG - Artículos de Revistas

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