Bayesian latent class analysis when the reference test is imperfect

Latent class analysis (LCA) has allowed epidemiologists to overcome the practical constraints faced by traditional diagnostic test evaluation methods, which require both a gold standard and ample numbers of appropriate reference samples. Over past four decades, LCA methods have expanded allow evaluate tests estimate true prevalence using imperfect over variety complex data structures scenarios, including during emergence novel infectious diseases. The objective this review is provide an overview recent developments in as well guide applying Bayesian (BLCA) tests. Before conducting BLCA, suitability BLCA for pathogen interest, availability samples, number tests, structure should be carefully considered. While formulating model, model’s specification informative priors will affect likelihood that useful inferences can drawn. With growing need advanced analytical newly emerging diseases, promising field research veterinary medical disciplines.