Estimators of sensitivity and specificity in the presence of verification bias: A Bayesian approach

Abstract

Verification bias can occur if some of the patients with test results are not selected to receive the gold standard procedure. Unverified cases frequently are not suggestive to be positives. Consequently, the set of verified cases overestimates the number of true positives and underestimates the number of true negatives. The sensitivity and specificity estimates based only on the patients with verified disease are often biased. In this article we derive estimators for sensitivity and specificity not subject to verification bias using a Bayesian approach. Marginal posterior densities of all parameters are estimated using the Gibbs sampler algorithm. An application to the study of accuracy of Hybrid Capture II in the diagnosis of cervical intraepithelial neoplasia grades 2 and 3 illustrates the proposed methodology.