IDEAS home Printed from https://ideas.repec.org/a/taf/japsta/v37y2010i8p1335-1354.html
   My bibliography  Save this article

On implementation of the Gibbs sampler for estimating the accuracy of multiple diagnostic tests

Author

Listed:
  • Fabio Principato
  • Angela Vullo
  • Domenica Matranga

Abstract

Implementation of the Gibbs sampler for estimating the accuracy of multiple binary diagnostic tests in one population has been investigated. This method, proposed by Joseph, Gyorkos and Coupal, makes use of a Bayesian approach and is used in the absence of a gold standard to estimate the prevalence, the sensitivity and specificity of medical diagnostic tests. The expressions that allow this method to be implemented for an arbitrary number of tests are given. By using the convergence diagnostics procedure of Raftery and Lewis, the relation between the number of iterations of Gibbs sampling and the precision of the estimated quantiles of the posterior distributions is derived. An example concerning a data set of gastro-esophageal reflux disease patients collected to evaluate the accuracy of the water siphon test compared with 24 h pH-monitoring, endoscopy and histology tests is presented. The main message that emerges from our analysis is that implementation of the Gibbs sampler to estimate the parameters of multiple binary diagnostic tests can be critical and convergence diagnostic is advised for this method. The factors which affect the convergence of the chains to the posterior distributions and those that influence the precision of their quantiles are analyzed.

Suggested Citation

  • Fabio Principato & Angela Vullo & Domenica Matranga, 2010. "On implementation of the Gibbs sampler for estimating the accuracy of multiple diagnostic tests," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(8), pages 1335-1354.
  • Handle: RePEc:taf:japsta:v:37:y:2010:i:8:p:1335-1354
    DOI: 10.1080/02664760903030239
    as

    Download full text from publisher

    File URL: http://www.tandfonline.com/doi/abs/10.1080/02664760903030239
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1080/02664760903030239?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Nandini Dendukuri & Lawrence Joseph, 2001. "Bayesian Approaches to Modeling the Conditional Dependence Between Multiple Diagnostic Tests," Biometrics, The International Biometric Society, vol. 57(1), pages 158-167, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hae-Young Kim & Michael G. Hudgens & Jonathan M. Dreyfuss & Daniel J. Westreich & Christopher D. Pilcher, 2007. "Comparison of Group Testing Algorithms for Case Identification in the Presence of Test Error," Biometrics, The International Biometric Society, vol. 63(4), pages 1152-1163, December.
    2. Scott Weichenthal & Lawrence Joseph & Patrick Bélisle & André Dufresne, 2010. "Bayesian Estimation of the Probability of Asbestos Exposure from Lung Fiber Counts," Biometrics, The International Biometric Society, vol. 66(2), pages 603-612, June.
    3. 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.
    4. Stamey, James D. & Boese, Doyle H. & Young, Dean M., 2008. "Confidence intervals for parameters of two diagnostic tests in the absence of a gold standard," Computational Statistics & Data Analysis, Elsevier, vol. 52(3), pages 1335-1346, January.
    5. O’Neill, Donal, 2015. "Measuring obesity in the absence of a gold standard," Economics & Human Biology, Elsevier, vol. 17(C), pages 116-128.
    6. 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.
    7. Elizabeth R. Brown, 2010. "Bayesian Estimation of the Time-Varying Sensitivity of a Diagnostic Test with Application to Mother-to-Child Transmission of HIV," Biometrics, The International Biometric Society, vol. 66(4), pages 1266-1274, December.
    8. Shahieda Adams & Rodney Ehrlich & Roslynn Baatjies & Nandini Dendukuri & Zhuoyu Wang & Keertan Dheda, 2019. "Evaluating Latent Tuberculosis Infection Test Performance Using Latent Class Analysis in a TB and HIV Endemic Setting," IJERPH, MDPI, vol. 16(16), pages 1-11, August.
    9. Paul S. Albert & Lori E. Dodd, 2004. "A Cautionary Note on the Robustness of Latent Class Models for Estimating Diagnostic Error without a Gold Standard," Biometrics, The International Biometric Society, vol. 60(2), pages 427-435, June.
    10. Martin Ladouceur & Elham Rahme & Christian A. Pineau & Lawrence Joseph, 2007. "Robustness of Prevalence Estimates Derived from Misclassified Data from Administrative Databases," Biometrics, The International Biometric Society, vol. 63(1), pages 272-279, March.
    11. Nandini Dendukuri & Ian Schiller & Lawrence Joseph & Madhukar Pai, 2012. "Bayesian Meta-Analysis of the Accuracy of a Test for Tuberculous Pleuritis in the Absence of a Gold Standard Reference," Biometrics, The International Biometric Society, vol. 68(4), pages 1285-1293, December.
    12. 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.
    13. 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.
    14. Leandro García Barrado & Els Coart & Tomasz Burzykowski, 2017. "Estimation of diagnostic accuracy of a combination of continuous biomarkers allowing for conditional dependence between the biomarkers and the imperfect reference-test," Biometrics, The International Biometric Society, vol. 73(2), pages 646-655, June.
    15. 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.
    16. Beavers, Daniel P. & Stamey, James D., 2012. "Bayesian sample size determination for binary regression with a misclassified covariate and no gold standard," Computational Statistics & Data Analysis, Elsevier, vol. 56(8), pages 2574-2582.
    17. Paul Gustafson & Sander Greenland, 2006. "The Performance of Random Coefficient Regression in Accounting for Residual Confounding," Biometrics, The International Biometric Society, vol. 62(3), pages 760-768, September.
    18. Gustafson Paul, 2010. "Bayesian Inference for Partially Identified Models," The International Journal of Biostatistics, De Gruyter, vol. 6(2), pages 1-20, March.
    19. 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.
    20. Min Zhang & Chong Wang & Annette O’Connor, 2021. "A Bayesian approach to modeling antimicrobial multidrug resistance," PLOS ONE, Public Library of Science, vol. 16(12), pages 1-14, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:taf:japsta:v:37:y:2010:i:8:p:1335-1354. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/CJAS20 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.