A review on the methodology for assessing diagnostic tests.

Abstract

Abstract Evaluation of diagnostic tests by the following principles are reviewed: error rates, scores based on posterior probabilities, and the excess loss considered in a decision theoretic context. Error rates or the complementary non-error rates, specificity and sensitivity, are simple measures which provide a rough indication of the discriminative value. In clinical practice, where a test serves as a decision support together with other information, conversion of test results to posterior probabilities is recommended. An aggregate score of these probabilities expresses the value of the test. Finally, in simple, well-defined cases--for example, screening situations, where the prevalence of disease and the relative consequences of false-positive and -negative classifications can be estimated--a Bayesian decision analysis is appropriate. The optimal discrimination limit is selected, and the total loss is minimized. The likelihood ratio LR(x) plays a central role in probability calculations and in the decision analysis. An example illustrates application of the procedures.