Skip to main page content
Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
, 57 (1), 158-67

Bayesian Approaches to Modeling the Conditional Dependence Between Multiple Diagnostic Tests


Bayesian Approaches to Modeling the Conditional Dependence Between Multiple Diagnostic Tests

N Dendukuri et al. Biometrics.


Many analyses of results from multiple diagnostic tests assume the tests are statistically independent conditional on the true disease status of the subject. This assumption may be violated in practice, especially in situations where none of the tests is a perfectly accurate gold standard. Classical inference for models accounting for the conditional dependence between tests requires that results from at least four different tests be used in order 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 inferences about the disease prevalence and test properties while adjusting for the possibility of conditional dependence between tests, particularly when we have only two tests. We propose both fixed and random effects models. Since with fewer than four tests the problem is nonidentifiable, the posterior distributions 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 precision, then our methods adjust for the dependence between the tests. Otherwise, our methods provide adjusted inferences that incorporate all of the uncertainty inherent in the problem, typically resulting in wider interval estimates. We illustrate our methods using data from a study on the prevalence of Strongyloides infection among Cambodian refugees to Canada.

Similar articles

See all similar articles

Cited by 88 PubMed Central articles

See all "Cited by" articles

Publication types

LinkOut - more resources