RANDOM EFFECTS MODELS IN LATENT CLASS ANALYSIS FOR EVALUATING ACCURACY OF DIAGNOSTIC-TESTS

When the results of a reference (or gold standard) test are missing or not error-free, the accuracy of diagnostic tests is often assessed th rough latent class models with two latent classes, representing diseas ed or nondiseased status. Such models, however, require that condition al on the true disease status, the tests are statistically independent , an assumption often violated in practice. Consequently, the model ge nerally fits the data poorly. In this paper, we develop a general late nt class model with random effects to model the conditional dependence among multiple diagnostic tests (or readers). We also develop a graph ical method for checking whether or not the conditional dependence is of concern and for identifying the pattern of the correlation. Using t he random-effects model and the graphical method, a simple adequate mo del that is easy to interpret can be obtained. The methods are illustr ated with three examples from the biometric literature. The proposed m ethodology is also applicable when the true disease status is indeed k nown and conditional dependence could well be present.