Premium
Case–Control Analysis with Partial Knowledge of Exposure Misclassification Probabilities
Author(s) -
Gustafson Paul,
Le Nhu D.,
Saskin Refik
Publication year - 2001
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.0006-341x.2001.00598.x
Subject(s) - bayes' theorem , statistics , odds , computer science , odds ratio , control (management) , prior probability , bayesian probability , mathematics , econometrics , artificial intelligence , logistic regression
Summary. 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.