A Bayesian approach to prospective binary outcome studies with misclassification in a binary risk factor

• Published in: Statistics in medicine Vol. 24; no. 22; pp. 3463 - 3477
• Main Authors: Prescott, G. J., Garthwaite, P. H.
• Format: Journal Article
• Language: English
• Published: Chichester, UK John Wiley & Sons, Ltd 30.11.2005; Wiley Subscription Services, Inc
• Subjects: Bayes Theorem; Bayesian analysis; Bias; binary outcome; Biometry; Clinical outcomes; Data Interpretation, Statistical; Disease; errors in variables; Female; Humans; Hypertension, Pregnancy-Induced - etiology; Logistic Models; measurement error; misclassification; Odds Ratio; Outcome Assessment (Health Care); Pregnancy; Prospective Studies; Risk Factors; Sensitivity and Specificity
• ISSN: 0277-6715; 1097-0258
• DOI: 10.1002/sim.2192

Misclassification in a binary exposure variable within an unmatched prospective study may lead to a biased estimate of the disease–exposure relationship. It usually gives falsely small credible intervals because uncertainty in the recorded exposure is not taken into account. When there are several other perfectly measured covariates, interrelationships may introduce further potential for bias. Bayesian methods are proposed for analysing binary outcome studies in which an exposure variable is sometimes misclassified, but its correct values have been validated for a random subsample of the subjects. This Bayesian approach can model relationships between explanatory variables and between exploratory variables and the probabilities of misclassification. Three logistic regressions are used to relate disease to true exposure, misclassified exposure to true exposure and true exposure to other covariates. Credible intervals may be used to make decisions about whether certain parameters are unnecessary and hence whether the model can be reduced in complexity. In the disease‐exposure model, for parameters representing coefficients related to perfectly measured covariates, the precision of posterior estimates is only slightly lower than would be found from data with no misclassification. For the risk factor which has misclassification, the estimates of model coefficients obtained are much less biased than those with misclassification ignored. Copyright © 2005 John Wiley & Sons, Ltd.