Factors That Influence Data Quality in Caries Experience Detection: A Multilevel Modeling Approach

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dc.contributor.authorMutsvari, T.
dc.contributor.authorLesaffre, E.
dc.contributor.authorGarcia Zattera, M. J.
dc.contributor.authorDiya, L.
dc.contributor.authorDeclerck, D.
dc.date.accessioned2024-01-10T13:48:20Z
dc.date.available2024-01-10T13:48:20Z
dc.date.issued2010
dc.description.abstractCaries experience detection is prone to misclassification. For this reason, calibration exercises which aim at assessing and improving the scoring behavior of dental raters are organized. During a calibration exercise, a sample of children is examined by the benchmark scorer and the dental examiners. This produces a 2 x 2 contingency table with the true and possibly misclassified responses. The entries in this misclassification table allow to estimate the sensitivity and the specificity of the raters. However, in many dental studies, the uncertainty with which sensitivity and specificity are estimated is not expressed. Further, caries experience data have a hierarchical structure since the data are recorded for the surfaces nested in the teeth within the mouth. Therefore, it is important to report the uncertainty using confidence intervals and to take the clustering into account. Here we apply a Bayesian logistic multilevel model for estimating the sensitivity and specificity. The main goal of this research is to find the factors that influence the true scoring of caries experience accounting for the hierarchical structure in the data. In our analysis, we show that the dentition type and tooth or surface type affect the quality of caries experience detection. Copyright (C) 2010 S. Karger AG, Basel
dc.description.funderCatholic University Leuven
dc.description.funderUnilever, Belgium
dc.description.funderNational Scholarship for Doctoral Studies
dc.fechaingreso.objetodigital2024-05-07
dc.format.extent7 páginas
dc.fuente.origenWOS
dc.identifier.doi10.1159/000319539
dc.identifier.eissn1421-976X
dc.identifier.issn0008-6568
dc.identifier.pubmedidMEDLINE:20838042
dc.identifier.urihttps://doi.org/10.1159/000319539
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/79356
dc.identifier.wosidWOS:000282173600003
dc.information.autorucMatemática;Garcia-Zattera MJ;S/I;14903
dc.issue.numero5
dc.language.isoen
dc.nota.accesocontenido parcial
dc.pagina.final444
dc.pagina.inicio438
dc.publisherKARGER
dc.revistaCARIES RESEARCH
dc.rightsacceso restringido
dc.subjectCalibration
dc.subjectMultilevel
dc.subjectSensitivity
dc.subjectSpecificity
dc.subjectDENTAL-CARIES
dc.subject.ods03 Good Health and Well-being
dc.subject.odspa03 Salud y bienestar
dc.titleFactors That Influence Data Quality in Caries Experience Detection: A Multilevel Modeling Approach
dc.typeartículo
dc.volumen44
sipa.codpersvinculados14903
sipa.indexWOS
sipa.indexScopus
sipa.trazabilidadCarga SIPA;09-01-2024
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