Estimation of sensitivity and specificity of diagnostic tests and disease prevalence when the true disease state is unknown

The performance of a new diagnostic test is frequently evaluated by compari son to a perfect reference test (i.e. a gold standard). In many instances, however, a reference test is less than perfect. In this paper, we review me thods for estimation of the accuracy of a diagnostic test when an imperfect reference test with known classification errors is available. Furthermore, we focus our presentation on available methods of estimation of test chara cteristics when the sensitivity and specificity of both tests are unknown. We present some of the available statistical methods for estimation of the accuracy of diagnostic tests when a reference test does not exist (includin g maximum likelihood estimation and Bayesian inference). We illustrate the application of the described methods using data from an evaluation of a nes ted polymerase chain reaction and microscopic examination of kidney imprint s for detection of Nucleospora salmonis in rainbow trout. (C) 2000 Elsevier Science B.V. All rights reserved.