If you need an accessible version of this item please contact JSTOR User Support

Case-Control Analysis with Partial Knowledge of Exposure Misclassification Probabilities

Paul Gustafson, Nhu D. Le and Refik Saskin
Biometrics
Vol. 57, No. 2 (Jun., 2001), pp. 598-609
Stable URL: http://www.jstor.org/stable/3068373
Page Count: 12
  • Download PDF
  • Cite this Item

You are not currently logged in.

Access your personal account or get JSTOR access through your library or other institution:

login

Log in to your personal account or through your institution.

If you need an accessible version of this item please contact JSTOR User Support
Case-Control Analysis with Partial Knowledge of Exposure Misclassification Probabilities
Preview not available

Abstract

Consider case-control analysis with a dichotomous exposure variable that is subject to misclassification. If the classification probabilities are known, then methods are available to adjust odds-ratio estimates in light of the misclassification. We study the realistic scenario where reasonable guesses, but not exact values, are available for the classification probabilities. If the analysis proceeds by simply treating the guesses as exact, then even small discrepancies between the guesses and the actual probabilities can seriously degrade odds-ratio estimates. We show that this problem is mitigated by a Bayes analysis that incorporates uncertainty about the classification probabilities as prior information.

Page Thumbnails

  • Thumbnail: Page 
598
    598
  • Thumbnail: Page 
599
    599
  • Thumbnail: Page 
600
    600
  • Thumbnail: Page 
601
    601
  • Thumbnail: Page 
602
    602
  • Thumbnail: Page 
603
    603
  • Thumbnail: Page 
604
    604
  • Thumbnail: Page 
605
    605
  • Thumbnail: Page 
606
    606
  • Thumbnail: Page 
607
    607
  • Thumbnail: Page 
608
    608
  • Thumbnail: Page 
609
    609