Abstract
The goal in diagnostic medicine is often to estimate the diagnostic accuracy of multiple experimental tests relative to a gold standard reference. When a gold standard reference is not available, investigators commonly use an imperfect reference standard. This paper proposes methodology for estimating the diagnostic accuracy of multiple binary tests with an imperfect reference standard when information about the diagnostic accuracy of the imperfect test is available from external data sources. We propose alternative joint models for characterizing the dependence between the experimental tests and discuss the use of these models for estimating individual-test sensitivity and specificity as well as prevalence and multivariate post-test probabilities (predictive values). We show using analytical and simulation techniques that, as long as the sensitivity and specificity of the imperfect test are high, inferences on diagnostic accuracy are robust to misspecification of the joint model. The methodology is demonstrated with a study examining the diagnostic accuracy of various HIV-antibody tests for HIV.