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A pseudo-likelihood approach for estimating diagnostic accuracy of multiple binary medical tests

Author

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  • Liu, Wei
  • Zhang, Bo
  • Zhang, Zhiwei
  • Chen, Baojiang
  • Zhou, Xiao-Hua

Abstract

Latent class models with crossed subject-specific and test(rater)-specific random effects have been proposed to estimate the diagnostic accuracy (sensitivity and specificity) of a group of binary tests or binary ratings. However, the computation of these models are hindered by their complicated Monte Carlo Expectation–Maximization (MCEM) algorithm. In this article, a class of pseudo-likelihood functions is developed for conducting statistical inference with crossed random-effects latent class models in diagnostic medicine. Theoretically, the maximum pseudo-likelihood estimation is still consistent and has asymptotic normality. Numerically, our results show that not only the pseudo-likelihood approach significantly reduces the computational time, but it has comparable efficiency relative to the MCEM algorithm. In addition, dimension-wise likelihood, one of the proposed pseudo-likelihoods, demonstrates its superior performance in estimating sensitivity and specificity.

Suggested Citation

  • Liu, Wei & Zhang, Bo & Zhang, Zhiwei & Chen, Baojiang & Zhou, Xiao-Hua, 2015. "A pseudo-likelihood approach for estimating diagnostic accuracy of multiple binary medical tests," Computational Statistics & Data Analysis, Elsevier, vol. 84(C), pages 85-98.
    • Handle: RePEc:eee:csdana:v:84:y:2015:i:c:p:85-98
    DOI: 10.1016/j.csda.2014.11.006
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    References listed on IDEAS

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    1. Bo Zhang & Zhen Chen & Paul S. Albert, 2012. "Estimating Diagnostic Accuracy of Raters Without a Gold Standard by Exploiting a Group of Experts," Biometrics, The International Biometric Society, vol. 68(4), pages 1294-1302, December.
    2. Albert, Paul S. & Dodd, Lori E., 2008. "On Estimating Diagnostic Accuracy From Studies With Multiple Raters and Partial Gold Standard Evaluation," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 61-73, March.
    3. Molenberghs, Geert & Verbeke, Geert & Iddi, Samuel, 2011. "Pseudo-likelihood methodology for partitioned large and complex samples," Statistics & Probability Letters, Elsevier, vol. 81(7), pages 892-901, July.
    4. 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.
    5. J. G. Booth & J. P. Hobert, 1999. "Maximizing generalized linear mixed model likelihoods with an automated Monte Carlo EM algorithm," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(1), pages 265-285.
    6. 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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    1. 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.

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