Bayesian Approaches to Modeling the Conditional Dependence Between Multiple Diagnostic Tests
Article first published online: 24 MAY 2004
Volume 57, Issue 1, pages 158–167, March 2001
How to Cite
Dendukuri, N. and Joseph, L. (2001), Bayesian Approaches to Modeling the Conditional Dependence Between Multiple Diagnostic Tests. Biometrics, 57: 158–167. doi: 10.1111/j.0006-341X.2001.00158.x
- Issue published online: 24 MAY 2004
- Article first published online: 24 MAY 2004
- Received June 1999. Revised July 2000. Accepted July 2000.
- Bayesian analysis;
- Binary data;
- Diagnostic tests;
- Gold standard;
- Latent class model;
- Markov chain Monte Carlo;
- Random effects model;
Summary. 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.