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A BAYESIAN APPROACH TO THE ASSESSMENT OF DIAGNOSTIC TEST ACCURACY IN THE ABSENCE OF A GOLD STANDARD

We have developed a Bayesian approach to estimating the sensitivity and specificity of multiple diagnostic tests (or human raters) in the absence of a gold standard. In many situations the responses of diagnostic tests are highly correlated given the presence or absence of a disease, which makes it difficult to estimate the accuracy of each test. In such cases the tests are said to be conditionally dependent given the true disease status of an individual. This is especially true in the case of human raters, who often receive similar training for determining disease status. The proposed methodology is based on the assumption of conditional dependence, and utilizes prior information about the sensitivities and specificities of the tests, as well as information about the population prevalence of the condition being tested for. The development of the model is discussed, and an example using multi-rater classification data is presented. The use of constraints introduced through prior distributions on the model parameters was found to be an effective way of dealing with a conditionally dependent group of raters.