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Correlation‐adjusted estimation of sensitivity and specificity of two diagnostic tests

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  • Marios P. Georgiadis
  • Wesley O. Johnson
  • Ian A. Gardner
  • Ramanpreet Singh

Abstract

Summary. Models for multiple‐test screening data generally require the assumption that the tests are independent conditional on disease state. This assumption may be unreasonable, especially when the biological basis of the tests is the same. We propose a model that allows for correlation between two diagnostic test results. Since models that incorporate test correlation involve more parameters than can be estimated with the available data, posterior inferences will depend more heavily on prior distributions, even with large sample sizes. If we have reasonably accurate information about one of the two screening tests (perhaps the standard currently used test) or the prevalences of the populations tested, accurate inferences about all the parameters, including the test correlation, are possible. We present a model for evaluating dependent diagnostic tests and analyse real and simulated data sets. Our analysis shows that, when the tests are correlated, a model that assumes conditional independence can perform very poorly. We recommend that, if the tests are only moderately accurate and measure the same biological responses, researchers use the dependence model for their analyses.

Suggested Citation

  • 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.
    • Handle: RePEc:bla:jorssc:v:52:y:2003:i:1:p:63-76
    DOI: 10.1111/1467-9876.00389
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    Citations

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    Cited by:

    1. Geoffrey Jones & Wesley O. Johnson, 2016. "A Bayesian Superpopulation Approach to Inference for Finite Populations Based on Imperfect Diagnostic Outcomes," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 21(2), pages 314-327, June.
    2. Gustafson Paul, 2010. "Bayesian Inference for Partially Identified Models," The International Journal of Biostatistics, De Gruyter, vol. 6(2), pages 1-20, March.
    3. Carol Y. Lin & Lance A. Waller & Robert H. Lyles, 2012. "The likelihood approach for the comparison of medical diagnostic system with multiple binary tests," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(7), pages 1437-1454, December.
    4. Xin Xia & Hui-Ping Zhu & Chuan-Hua Yu & Xing-Jian Xu & Ren-Dong Li & Juan Qiu, 2013. "A Bayesian Approach to Estimate the Prevalence of Schistosomiasis japonica Infection in the Hubei Province Lake Regions, China," IJERPH, MDPI, vol. 10(7), pages 1-14, July.
    5. Adam J. Branscum & Dunlei Cheng & J. Jack Lee, 2015. "Testing hypotheses about medical test accuracy: considerations for design and inference," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(5), pages 1106-1119, May.
    6. 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.
    7. Geoffrey Jones & Wesley O. Johnson & Timothy E. Hanson & Ronald Christensen, 2010. "Identifiability of Models for Multiple Diagnostic Testing in the Absence of a Gold Standard," Biometrics, The International Biometric Society, vol. 66(3), pages 855-863, September.
    8. Pierre Bessière & Brandon Hayes & Fabien Filaire & Laetitia Lèbre & Timothée Vergne & Matthieu Pinson & Guillaume Croville & Jean-Luc Guerin, 2023. "Optimizing environmental viral surveillance: bovine serum albumin increases RT-qPCR sensitivity for high pathogenicity avian influenza H5Nx virus detection from dust samples," Post-Print hal-04335181, HAL.
    9. Adam Branscum & Timothy Hanson & Ian Gardner, 2008. "Bayesian non-parametric models for regional prevalence estimation," Journal of Applied Statistics, Taylor & Francis Journals, vol. 35(5), pages 567-582.
    10. Caitlin Ward & Grant D. Brown & Jacob J. Oleson, 2023. "An individual level infectious disease model in the presence of uncertainty from multiple, imperfect diagnostic tests," Biometrics, The International Biometric Society, vol. 79(1), pages 426-436, March.

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