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      Modeling the Accuracy of Two in-vitro Bovine Tuberculosis Tests Using a Bayesian Approach

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          Abstract

          Accuracy of new or alternative diagnostic tests is typically estimated in relation to a well-standardized reference test referred to as a gold standard. However, for bovine tuberculosis (bTB), a chronic disease of cattle, affecting animal and public health, no reliable gold standard is available. In this context, latent-class models implemented using a Bayesian approach can help to assess the accuracy of diagnostic tests incorporating previous knowledge on test performance and disease prevalence. In Uruguay, bTB-prevalence has increased in the past decades partially because of the limited accuracy of the diagnostic strategy in place, based on intradermal testing (caudal fold test, CFT, for screening and comparative cervical test, CCT, for confirmation) and slaughter of reactors. Here, we evaluated the performance of two alternative bTB-diagnostic tools, the interferon-gamma assay, IGRA, and the enzyme-linked immunosorbent assay (ELISA), which had never been used in Uruguay in the absence of a gold standard. In order to do so animals from two heavily infected dairy herds and tested with CFT-CCT were also analyzed with the IGRA using two antigens (study 1) and the ELISA (study 2). The accuracy of the IGRA and ELISA was assessed fitting two latent-class models: a two test-one population model (LCA-a) based on the analysis of CFT/CFT-CCT test results and one in-vitro test (IGRA/ELISA), and a one test-one population model (LCA-b) using the IGRA or ELISA information in which the prevalence was modeled using information from the skin tests. Posterior estimates for model LCA-a suggested that IGRA was as sensitive (75–78%) as the CFT and more sensitive than the serial use of CFT-CCT. Its specificity (90–96%) was superior to the one for the CFT and equivalent to the use of CFT-CCT. Estimates from LCA-b models consistently yielded lower posterior Se estimates for the IGRA but similar results for its Sp. Estimates for the Se (52% 95%PPI:44.41-71.28) and the Sp (92% 95%PPI:78.63–98.76) of the ELISA were however similar regardless of the model used. These results suggest that the incorporation of IGRA for detection of bTB in highly infected herds could be a useful tool to improve the sensitivity of the bTB-control in Uruguay.

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          Inference from Iterative Simulation Using Multiple Sequences

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            Case-control and two-gate designs in diagnostic accuracy studies.

            In some diagnostic accuracy studies, the test results of a series of patients with an established diagnosis are compared with those of a control group. Such case-control designs are intuitively appealing, but they have also been criticized for leading to inflated estimates of accuracy. We discuss similarities and differences between diagnostic and etiologic case-control studies, as well as the mechanisms that can lead to variation in estimates of diagnostic accuracy in studies with separate sampling schemes ("gates") for diseased (cases) and nondiseased individuals (controls). Diagnostic accuracy studies are cross-sectional and descriptive in nature. Etiologic case-control studies aim to quantify the effect of potential causal exposures on disease occurrence, which inherently involves a time window between exposure and disease occurrence. Researchers and readers should be aware of spectrum effects in diagnostic case-control studies as a result of the restricted sampling of cases and/or controls, which can lead to changes in estimates of diagnostic accuracy. These spectrum effects may be advantageous in the early investigation of a new diagnostic test, but for an overall evaluation of the clinical performance of a test, case-control studies should closely mimic cross-sectional diagnostic studies. As the accuracy of a test is likely to vary across subgroups of patients, researchers and clinicians might carefully consider the potential for spectrum effects in all designs and analyses, particularly in diagnostic accuracy studies with differential sampling schemes for diseased (cases) and nondiseased individuals (controls).
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              Estimation of diagnostic-test sensitivity and specificity through Bayesian modeling.

              We review recent Bayesian approaches to estimation (based on cross-sectional sampling designs) of the sensitivity and specificity of one or more diagnostic tests. Our primary goal is to provide veterinary researchers with a concise presentation of the computational aspects involved in using the Bayesian framework for test evaluation. We consider estimation of diagnostic-test sensitivity and specificity in the following settings: (i) one test in one population, (ii) two conditionally independent tests in two or more populations, (iii) two correlated tests in two or more populations, and (iv) three tests in two or more populations, where two tests are correlated but jointly independent of the third test. For each scenario, we describe a Bayesian model that incorporates parameters of interest. The WinBUGS code used to fit each model, which is available at http://www.epi.ucdavis.edu/diagnos-tictests/, can be altered readily to conform to different data.
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                Author and article information

                Contributors
                Journal
                Front Vet Sci
                Front Vet Sci
                Front. Vet. Sci.
                Frontiers in Veterinary Science
                Frontiers Media S.A.
                2297-1769
                13 August 2019
                2019
                : 6
                : 261
                Affiliations
                [1] 1Department of Veterinary Population Medicine, University of Minnesota , Saint Paul, MN, United States
                [2] 2Facultad de Veterinaria, Universidad de la Republica , Montevideo, Uruguay
                [3] 3División Laboratorios Veterinarios “Miguel C. Rubino”, Ministerio de Ganadería, Agricultura y Pesca , Montevideo, Uruguay
                [4] 4VISAVET Health Surveillance Centre, Universidad Complutense , Madrid, Spain
                [5] 5Departamento de Sanidad Animal, Facultad de Veterinaria, Universidad Complutense de Madrid , Madrid, Spain
                Author notes

                Edited by: Alejandra Victoria Capozzo, National Council for Scientific and Technical Research (CONICET), Argentina

                Reviewed by: Wendy Beauvais, Cornell University, United States; Ignacio De Blas, University of Zaragoza, Spain

                *Correspondence: Catalina Picasso-Risso picas001@ 123456umn.edu

                This article was submitted to Veterinary Epidemiology and Economics, a section of the journal Frontiers in Veterinary Science

                Article
                10.3389/fvets.2019.00261
                6701407
                31457019
                47aa546b-0507-47c5-930c-a095232f21a9
                Copyright © 2019 Picasso-Risso, Perez, Gil, Nunez, Salaberry, Suanes and Alvarez.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 02 April 2019
                : 25 July 2019
                Page count
                Figures: 1, Tables: 3, Equations: 0, References: 64, Pages: 9, Words: 7052
                Categories
                Veterinary Science
                Original Research

                latent class analysis,diagnosis,interferon-gamma release assay,elisa,chronically infected,uruguay

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