Bayesian Latent Class Analysis for Diagnostic Test Evaluation
Date: Thursday 25 October 2018
Time: 11:00 a.m.
Place: Room 241, 2nd floor, Science III building
Evaluating the performance of diagnostic tests for infection or disease is of crucial importance, both for the treatment of individuals and for the monitoring of populations. In many situations there is no “gold standard” test that can be relied upon to give 100% accuracy, and the use of a particular test will typically lead to false positives or false negative outcomes. The performance characteristics of an imperfect test are summarized by its sensitivity, i.e. the probability of correct diagnosis for a diseased individual, and its specificity i.e. the probability of a correct diagnosis when disease-free. When these parameters are known, valid statistical inference can be made for the disease status of tested individuals and the prevalence of disease in a monitored population. In the absence of a “gold standard”, true disease status is unobservable so the sensitivity and specificity cannot be reliably determined in the absence of additional information. In some circumstances, information from a number of imperfect tests allows estimation of the prevalence, sensitivities and specificities even in the absence of gold standard data. Latent class analysis in a Bayesian framework gives a flexible and comprehensive way of doing this which has become common in the epidemiology literature. This talk will give an introduction and review of the basic ideas, and highlight some of the current research in this area.