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