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

作者：Liu, Wei; Zhang, Bo^*; Zhang, Zhiwei; Chen, Baojiang; Zhou, Xiao-Hua

来源：Computational Statistics & Data Analysis, 2015, 84: 85-98.

DOI：10.1016/j.csda.2014.11.006

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

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. Published by Elsevier B.V.

• 出版日期2015-4
• 单位哈尔滨工业大学