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Theory and methods
A simple model for potential use with a misclassified binary outcome in epidemiology
  1. S W Duffy1,
  2. J Warwick1,
  3. A R W Williams2,
  4. H Keshavarz3,
  5. F Kaffashian4,
  6. T E Rohan5,
  7. F Nili6,
  8. A Sadeghi-Hassanabadi6
  1. 1Cancer Research UK Department of Epidemiology, Mathematics and Statistics, Wolfson Institute of Preventive Medicine, Queen Mary University of London, London, UK
  2. 2Department of Pathology, University of Edinburgh, UK
  3. 3Centers for Disease Control, Atlanta, USA
  4. 4Cancer Intelligence Unit, Strangeways Research Laboratory, Cambridge, UK
  5. 5Department of Epidemiology and Social Medicine, Albert Einstein College of Medicine, New York, USA
  6. 6Shiraz University of Medical Sciences, Shiraz, Iran
  1. Correspondence to:
 Professor S W Duffy
 Cancer Research UK Department of Epidemiology, Mathematics and Statistics, Wolfson Institute of Preventive Medicine, Queen Mary University of London, Charterhouse Square, London EC1M 6BQ, UK; stephen.duffycancer.org.uk

Abstract

Study objective: Error in determination of disease outcome occurs in epidemiology, but such error is not usually corrected for in statistical analysis. A method of correction of risk estimates for misclassification of a binary disease outcome is developed here.

Methods: The method is a simple, closed form correction to the logistic regression estimate. A closed form variance estimate is also developed.

Setting: The method is illustrated in two studies, a cross sectional survey of cervicitis in Iran in 1996–97, as determined by inflammation on cervical smear specimens, and a case-cohort study of benign proliferative epithelial disease of the breast, in Canada 1980–88.

Main results: The method provides corrected odds ratio estimates and corrects the spurious precision conferred by misclassification.

Conclusions: The method is easy to apply and potentially useful, although potential failures of the assumptions involved should be borne in mind. It is necessary to give careful consideration to the plausibility or otherwise of the assumptions in the context of the individual study. Correction for misclassification of disease outcome may become more common with the development of readily applicable methods.

  • BV, bacterial vaginosis
  • BPBD, benign proliferative breast disease
  • misclassification of disease outcome
  • logistic regression
  • log odds ratio

https://doi.org/10.1136/jech.2003.010546

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    Footnotes

    • Funding: We thank the estate of the late Ali Reza Soudavar for financial support for Homa Keshavarz.

    • Conflicts of interest: none declared.

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