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

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

Computational Statistics & Data Analysis, 2015, vol. 84, issue C, 85-98

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.

Keywords: Sensitivity and specificity; Random effects; Latent class models; Composite likelihood; Imperfect reference standards (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:84:y:2015:i:c:p:85-98

DOI: 10.1016/j.csda.2014.11.006

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