Correction for misclassification of caries experience in the absence of internal validation data

T Mutsvari, D Declerck, Emmanuel Lesaffre

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3 Citations (Scopus)


To quantify the effects of risk factors and/or determinants on disease occurrence, it is important that the risk factors as well as the variable that measures the disease outcome are recorded with the least error as possible. When investigating the factors that influence a binary outcome, a logistic regression model is often fitted under the assumption that the data are collected without error. However, most categorical outcomes (e.g., caries experience) are accompanied by misclassification and this needs to be accounted for. The aim of this research was to adjust for binary outcome misclassification using an external validation study when investigating factors influencing caries experience in schoolchildren. Data from the Signal TandmobielA (R) study were used. A total of 500 children from the main and 148 from the validation study were included in the analysis. Regression models (with several covariates) for sensitivity and specificity were used to adjust for misclassification in the main data. The use of sensitivity and specificity modeled as functions of several covariates resulted in a better correction compared to using point estimates of sensitivity and specificity. Age, geographical location of the school to which the child belongs, dentition type, tooth type, and surface type were significantly associated with the prevalence of caries experience. Sensitivity and specificity calculated based on an external validation study may resemble those obtained from an internal study if conditioned on a rich set of covariates. Main data can be corrected for misclassification using information obtained from an external validation study when a rich set of covariates is recorded during calibration.
Original languageUndefined/Unknown
Pages (from-to)1799-1805
Number of pages7
JournalClinical Oral Investigations
Issue number8
Publication statusPublished - 2013

Research programs

  • EMC NIHES-01-66-01

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