A Bayesian approach to model the conditional correlation between several diagnostic tests and various replicated subjects measurements.

Two key aims of diagnostic research are to accurately and precisely estimate disease prevalence and test sensitivity and specificity. Latent class models have been proposed that consider the correlation between subject measures determined by different tests in order to diagnose diseases for which gold standard tests are not available. In some clinical studies, several measures of the same subject are made with the same test under the same conditions (replicated measurements), and thus, replicated measurements for each subject are not independent. In the present study, we propose an extension of the Bayesian latent class Gaussian random effects model to fit the data with binary outcomes for tests with replicated subject measures. We describe an application using data collected on hookworm infection carried out in the municipality of Presidente Figueiredo, Amazonas State, Brazil. In addition, the performance of the proposed model was compared with that of current models (the subject random effects model and the conditional (in) dependent model) through a simulation study. As expected, the proposed model presented better accuracy and precision in the estimations of prevalence, sensitivity and specificity. Copyright (C) 2017 John Wiley & Sons, Ltd.