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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/6962

Title: A Bayesian ordinal logistic regression model to correct for interobserver measurement error in a geographical oral health study
Authors: LESAFFRE, Emmanuel
Mwalili, Samuel M.
Declerck, Dominique
Issue Date: 2005
Citation: Journal of the Royal Statistical Society: Series C Applied statistics, 54(1). p. 77-93
Abstract: We present an approach for correcting for interobserver measurement error in an ordinal logistic regression model taking into account also the variability of the estimated correction terms. The different scoring behaviour of the 16 examiners complicated the identification of a geographical trend in a recent study on caries experience in Flemish children (Belgium) who were 7 years old. Since the measurement error is on the response the factor 'examiner' could be included in the regression model to correct for its confounding effect. However, controlling for examiner largely removed the geographical east-west trend. Instead, we suggest a (Bayesian) ordinal logistic model which corrects for the scoring error (compared with a gold standard) using a calibration data set. The marginal posterior distribution of the regression parameters of interest is obtained by integrating out the correction terms pertaining to the calibration data set. This is done by processing two Markov chains sequentially, whereby one Markov chain samples the correction terms. The sampled correction term is imputed in the Markov chain pertaining to the regression parameters. The model was fitted to the oral health data of the Signal-Tandmobiel┬« study. A WinBUGS program was written to perform the analysis.
Bayesian analysis; calibration exercise; errors in variables; Markov chain Monte Carlo methods; measurement error; ordinal logistic regression
URI: http://hdl.handle.net/1942/6962
DOI: 10.1111/j.1467-9876.2005.00471.x
ISI #: 000224645300006
Category: A1
Type: Journal Contribution
Appears in Collections: Research publications

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