title: STARD-BLCM: Standards for the Reporting of Diagnostic accuracy studies that use Bayesian Latent Class Models.
abstract: The Standards for the Reporting of Diagnostic Accuracy (STARD) statement, which was recently updated to the STARD2015 statement, was developed to encourage complete and transparent reporting of test accuracy studies. Although STARD principles apply broadly, the checklist is limited to studies designed to evaluate the accuracy of tests when the disease status is determined from a perfect reference procedure or an imperfect one with known measures of test accuracy. However, a reference standard does not always exist, especially in the case of infectious diseases with a long latent period. In such cases, a valid alternative to classical test evaluation involves the use of latent class models that do not require a priori knowledge of disease status. Latent class models have been successfully implemented in a Bayesian framework for over 20 years. The objective of this work was to identify the STARD items that require modification and develop a modified version of STARD for studies that use Bayesian latent class analysis to estimate diagnostic test accuracy in the absence of a reference standard. Examples and elaborations for each of the modified items are provided. The new guidelines, termed STARD-BLCM (Standards for Reporting of Diagnostic accuracy studies that use Bayesian Latent Class Models), will facilitate improved quality of reporting on the design, conduct and results of diagnostic accuracy studies that use Bayesian latent class models.
authors.affiliation: Laboratory of Epidemiology, Biostatistics and Animal Health Economics, Faculty of Veterinary Medicine, University of Thessaly, Karditsa GR43100, Greece. Electronic address: pkost@vet.uth.gr.
authors.forename: Polychronis
authors.initials: P
authors.lastname: Kostoulas
authors.affiliation: Department of Veterinary and Animal Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Grønnegårdsvej 8, DK-1870 Frederiksberg C, Denmark.
authors.forename: Søren S
authors.initials: SS
authors.lastname: Nielsen
authors.affiliation: Biostatistics Program, Oregon State University, Corvallis, OR, 97331, USA.
authors.forename: Adam J
authors.initials: AJ
authors.lastname: Branscum
authors.affiliation: Department of Statistics, University of California, Irvine, CA, 92697, USA.
authors.forename: Wesley O
authors.initials: WO
authors.lastname: Johnson
authors.affiliation: McGill University Health Centre, McGill University, Montréal, QC, Canada.
authors.forename: Nandini
authors.initials: N
authors.lastname: Dendukuri
authors.affiliation: Faculty of Veterinary Science, The University of Sydney, 425 Werombi Road, Camden, 2570 NSW, Australia.
authors.forename: Navneet K
authors.initials: NK
authors.lastname: Dhand
authors.affiliation: Technical University of Denmark, National Veterinary Institute, Bülowsvej 27, DK-1870 Frederiksberg C, Denmark.
authors.forename: Nils
authors.initials: N
authors.lastname: Toft
authors.affiliation: Department of Health Management, Atlantic Veterinary College, University of Prince Edward Island, Charlottetown, Prince Edward Island C1A4P3, Canada.
authors.forename: Ian A
authors.initials: IA
authors.lastname: Gardner
mesh_terms: D000818:Animals; D001499:Bayes Theorem; D000068598:Data Accuracy; D003955:Diagnostic Tests, Routine / Q000592:standards*; D004797:Enzyme-Linked Immunosorbent Assay / Q000592:standards / Q000662:veterinary; D017408:Guidelines as Topic; D006801:Humans; D016133:Polymerase Chain Reaction / Q000592:standards / Q000662:veterinary; D012016:Reference Values; D012680:Sensitivity and Specificity
keywords: Bayesian analysis; Latent class models; Sensitivity; Specificity