IDEAS home Printed from https://ideas.repec.org/a/bla/jorssc/v54y2005i1p77-93.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/j.1467-9876.2005.00471.x
    Download Restriction: no

    File URL: https://libkey.io/10.1111/j.1467-9876.2005.00471.x?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.
    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.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bla:jorssc:v:54:y:2005:i:1:p:77-93. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: https://edirc.repec.org/data/rssssea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.