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

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

Research output: Contribution to journalArticle

2 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)85-98
Number of pages14
JournalComputational Statistics and Data Analysis
Publication statusPublished - Apr 2015



  • Composite likelihood
  • Imperfect reference standards
  • Latent class models
  • Random effects
  • Sensitivity and specificity

ASJC Scopus subject areas

  • Statistics and Probability
  • Computational Mathematics
  • Computational Theory and Mathematics
  • Applied Mathematics

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