Bayesian approaches to modeling the conditional dependence between multiple diagnostic tests

Many analyses of results from multiple diagnostic tests assume the tests ar e statistically independent conditional on the true disease status of the s ubject. This assumption may be violated in practice, especially in situatio ns where none of the tests is a perfectly accurate gold standard. Classical inference for models accounting for the conditional dependence between tes ts requires that results from at least four different tests be used in orde r to obtain an identifiable solution, but it is not always feasible to have results from this many tests. We use a Bayesian approach to draw inference s about the disease prevalence and test properties while adjusting for the possibility of conditional dependence between tests, particularly when we h ave only two tests. We propose both fixed and random effects models. Since with fewer than four tests the problem is nonidentifiable, the posterior di stributions are strongly dependent on the prior information about the test properties and the disease prevalence, even with large sample sizes. If the degree of correlation between the tests is known a priori with high precis ion, then our methods adjust for the dependence between the tests. Otherwis e, our methods provide adjusted inferences that incorporate all of the unce rtainty inherent in the problem, typically resulting in wider interval esti mates. We illustrate our methods using data from a study on the prevalence of Strongyloides infection among Cambodian refugees to Canada.