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An intercomparison study of ELISAs for the detection of porcine reproductive and respiratory syndrome virus – evaluating six conditionally dependent tests

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  • Clara Schoneberg
  • Jens Böttcher
  • Britta Janowetz
  • Anja Rostalski
  • Lothar Kreienbrock
  • Amely Campe

Abstract

Latent class analysis is a widely used statistical method for evaluating diagnostic tests without any gold standard. It requires the results of at least two tests applied to the same individuals. Based on the resulting response patterns, the method estimates the test accuracy and the unknown disease status for all individuals in the sample. An important assumption is the conditional independence of the tests. If tests with the same biological principle are used, the assumption is not fulfilled, which may lead to biased results. In a recent publication, we developed a method that considers the dependencies in the latent class model and estimates all parameters using frequentist methods. Here, we evaluate the practicability of the method by applying it to the results of six ELISA tests for antibodies against the porcine reproductive and respiratory syndrome (PRRS) virus in pigs that generally follow the same biological principle. First, we present different methods of identifying suitable starting values for the algorithm and apply these to the dataset and a vaccinated subgroup. We present the calculated values of the test accuracies, the estimated proportion of antibody-positive animals and the dependency structure for both datasets. Different starting values led to matching results for the entire dataset. For the vaccinated subgroup, the results were more dependent on the selected starting values. All six ELISA tests are well suited to detect antibodies against PRRS virus, whereas none of the tests had the best values for sensitivity and specificity simultaneously. The results thus show that the method used is able to determine the parameter values of conditionally dependent tests with suitable starting values. The choice of test should be based on the general fit-for-purpose concept and the population under study.

Suggested Citation

  • Clara Schoneberg & Jens Böttcher & Britta Janowetz & Anja Rostalski & Lothar Kreienbrock & Amely Campe, 2022. "An intercomparison study of ELISAs for the detection of porcine reproductive and respiratory syndrome virus – evaluating six conditionally dependent tests," PLOS ONE, Public Library of Science, vol. 17(1), pages 1-16, January.
    • Handle: RePEc:plo:pone00:0262944
    DOI: 10.1371/journal.pone.0262944
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    References listed on IDEAS

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    1. Marios P. Georgiadis & Wesley O. Johnson & Ian A. Gardner & Ramanpreet Singh, 2003. "Correlation‐adjusted estimation of sensitivity and specificity of two diagnostic tests," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 52(1), pages 63-76, January.
    2. Paul S. Albert & Lisa M. McShane & Joanna H. Shih, 2001. "Latent Class Modeling Approaches for Assessing Diagnostic Error without a Gold Standard: With Applications to p53 Immunohistochemical Assays in Bladder Tumors," Biometrics, The International Biometric Society, vol. 57(2), pages 610-619, June.
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