The accuracy of a diagnostic test or combination of tests (such as in a diagnostic model) is the ability to correctly identify patients with or without the target disease. In studies of diagnostic accuracy, the results of the test or model under study are verified by comparing them with results of a reference standard, applied to the same patients, to verify disease status (see first panel in figure⇓).1 Measures such as predictive values, post-test probabilities, ROC (receiver operating characteristics) curves, sensitivity, specificity, likelihood ratios, and odds ratios express how well the results of an index test agree with the outcome of the reference standard.2 Biased and exaggerated estimates of diagnostic accuracy can lead to inefficiencies in diagnostic testing in practice, unnecessary costs, and physicians making incorrect treatment decisions.

The reference standard ideally provides error-free classification of the disease outcome presence or absence. In some cases, it is not possible to verify the definitive presence or absence of disease in all patients with the (single) reference standard, which may result in bias. In this paper, we describe the most important types of disease verification problems using examples from published diagnostic accuracy studies. We also propose solutions to alleviate the associated biases.