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Simplifying the estimation of diagnostic testing accuracy over time for high specificity tests in the absence of a gold standard

Author

Listed:
  • Clara Drew
  • Moses Badio
  • Dehkontee Dennis
  • Lisa Hensley
  • Elizabeth Higgs
  • Michael Sneller
  • Mosoka Fallah
  • Cavan Reilly

Abstract

Many different methods for evaluating diagnostic test results in the absence of a gold standard have been proposed. In this paper, we discuss how one common method, a maximum likelihood estimate for a latent class model found via the Expectation‐Maximization (EM) algorithm can be applied to longitudinal data where test sensitivity changes over time. We also propose two simplified and nonparametric methods which use data‐based indicator variables for disease status and compare their accuracy to the maximum likelihood estimation (MLE) results. We find that with high specificity tests, the performance of simpler approximations may be just as high as the MLE.

Suggested Citation

  • Clara Drew & Moses Badio & Dehkontee Dennis & Lisa Hensley & Elizabeth Higgs & Michael Sneller & Mosoka Fallah & Cavan Reilly, 2023. "Simplifying the estimation of diagnostic testing accuracy over time for high specificity tests in the absence of a gold standard," Biometrics, The International Biometric Society, vol. 79(2), pages 1546-1558, June.
    • Handle: RePEc:bla:biomet:v:79:y:2023:i:2:p:1546-1558
    DOI: 10.1111/biom.13689
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    References listed on IDEAS

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    1. Huiping Xu & Bruce A. Craig, 2009. "A Probit Latent Class Model with General Correlation Structures for Evaluating Accuracy of Diagnostic Tests," Biometrics, The International Biometric Society, vol. 65(4), pages 1145-1155, December.
    2. Chinyereugo M Umemneku Chikere & Kevin Wilson & Sara Graziadio & Luke Vale & A Joy Allen, 2019. "Diagnostic test evaluation methodology: A systematic review of methods employed to evaluate diagnostic tests in the absence of gold standard – An update," PLOS ONE, Public Library of Science, vol. 14(10), pages 1-25, October.
    3. 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.
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    5. 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.
    6. Chu, Haitao & Chen, Sining & Louis, Thomas A., 2009. "Random Effects Models in a Meta-Analysis of the Accuracy of Two Diagnostic Tests Without a Gold Standard," Journal of the American Statistical Association, American Statistical Association, vol. 104(486), pages 512-523.
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