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A Bayesian ordinal logistic regression model to correct for interobserver measurement error in a geographical oral health study

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  • Samuel M. Mwalili
  • Emmanuel Lesaffre
  • Dominique Declerck

Abstract

Summary. 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.

Suggested Citation

  • Samuel M. Mwalili & Emmanuel Lesaffre & Dominique Declerck, 2005. "A Bayesian ordinal logistic regression model to correct for interobserver measurement error in a geographical oral health study," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 54(1), pages 77-93, January.
  • Handle: RePEc:bla:jorssc:v:54:y:2005:i:1:p:77-93
    DOI: 10.1111/j.1467-9876.2005.00471.x
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    Cited by:

    1. Helmut Küchenhoff & Samuel M. Mwalili & Emmanuel Lesaffre, 2006. "A General Method for Dealing with Misclassification in Regression: The Misclassification SIMEX," Biometrics, The International Biometric Society, vol. 62(1), pages 85-96, March.
    2. Ivy Liu & Alan Agresti, 2005. "The analysis of ordered categorical data: An overview and a survey of recent developments," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 14(1), pages 1-73, June.
    3. Satoshi Morita & Peter F. Thall & B. Nebiyou Bekele & Paul Mathew, 2010. "A Bayesian hierarchical mixture model for platelet‐derived growth factor receptor phosphorylation to improve estimation of progression‐free survival in prostate cancer," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 59(1), pages 19-34, January.

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