Research Article

Modelling risk when binary outcomes are subject to error

  1. Pat McInturff1,
  2. Wesley O Johnson2,*,
  3. David Cowling3,
  4. Ian A Gardner1

Article first published online: 23 MAR 2004

DOI: 10.1002/sim.1656

Issue

Statistics in Medicine

Statistics in Medicine

Volume 23, Issue 7, pages 1095–1109, 15 April 2004

Additional Information

How to Cite

McInturff, P., Johnson, W. O., Cowling, D. and Gardner, I. A. (2004), Modelling risk when binary outcomes are subject to error. Statistics in Medicine, 23: 1095–1109. doi: 10.1002/sim.1656

Author Information

  1. 1

    Department of Medicine and Epidemiology, School of Veterinary Medicine, University of California, One Shields Ave, Davis, CA 95616, U.S.A.

  2. 2

    Department of Statistics, University of California, One Shields Ave, Davis, CA 95616, U.S.A.

  3. 3

    Tobacco Control Section, California Department of Health Services, P.O.B. 942732, MS 555 Sacramento, CA 94234-7320, U.S.A.

*Department of Statistics, University of California, One Shields Avenue, Davis, CA 95616, U.S.A.

Publication History

  1. Issue published online: 23 MAR 2004
  2. Article first published online: 23 MAR 2004
  3. Manuscript Accepted:
  4. Manuscript Received:

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Keywords:

  • Bayesian;
  • sensitivity;
  • specificity;
  • logistic regression;
  • Markov chain Monte Carlo;
  • Bayes factor

Abstract

We present methods for binomial regression when the outcome is determined using the results of a single diagnostic test with imperfect sensitivity and specificity. We present our model, illustrate it with the analysis of real data, and provide an example of WinBUGS program code for performing such an analysis. Conditional means priors are used in order to allow for inclusion of prior data and expert opinion in the estimation of odds ratios, probabilities, risk ratios, risk differences, and diagnostic test sensitivity and specificity. A simple method of obtaining Bayes factors for link selection is presented. Methods are illustrated and compared with Bayesian ordinary binary regression using data from a study of the effectiveness of a smoking cessation program among pregnant women. Regression coefficient estimates are shown to change noticeably when expert prior knowledge and imperfect sensitivity and specificity are incorporated into the model. Copyright © 2004 John Wiley & Sons, Ltd.

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