title: Estimation of diagnostic test accuracy without full verification: a review of latent class methods.
abstract: The performance of a diagnostic test is best evaluated against a reference test that is without error. For many diseases, this is not possible, and an imperfect reference test must be used. However, diagnostic accuracy estimates may be biased if inaccurately verified status is used as the truth. Statistical models have been developed to handle this situation by treating disease as a latent variable. In this paper, we conduct a systematized review of statistical methods using latent class models for estimating test accuracy and disease prevalence in the absence of complete verification.
authors.affiliation: Rehabilitation Medicine Department, National Institutes of Health, Bethesda MD 20892, U.S.A.
authors.forename: John
authors.initials: J
authors.lastname: Collins
authors.forename: Minh
authors.initials: M
authors.lastname: Huynh
mesh_terms: D000818:Animals; D015982:Bias; D003955:Diagnostic Tests, Routine / Q000379:methods* / Q000592:standards; D006801:Humans; D015233:Models, Statistical; D012015:Reference Standards; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
keywords: diagnostic testing; latent class model; no gold standard; review; sensitivity; specificity