On composite likelihood in bivariate meta-analysis of diagnostic test accuracy studies

Nikoloulopoulos, Aristidis K. ORCID: https://orcid.org/0000-0003-0853-0084 (2018) On composite likelihood in bivariate meta-analysis of diagnostic test accuracy studies. AStA Advances in Statistical Analysis, 102 (2). pp. 211-227. ISSN 1863-8171

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Abstract

The composite likelihood (CL) is amongst the computational methods used for estimation of the generalized linear mixed model (GLMM) in the context of bivariate meta-analysis of diagnostic test accuracy studies. Its advantage is that the likelihood can be derived conveniently under the assumption of independence between the random effects, but there has not been a clear analysis of the merit or necessity of this method. For synthesis of diagnostic test accuracy studies, a copula mixed model has been proposed in the biostatistics literature. This general model includes the GLMM as a special case and can also allow for flexible dependence modelling, different from assuming simple linear correlation structures, normality and tail independence in the joint tails. A maximum likelihood (ML) method, which is based on evaluating the bi-dimensional integrals of the likelihood with quadrature methods has been proposed, and in fact it eases any computational difficulty that might be caused by the double integral in the likelihood function. Both methods are thoroughly examined with extensive simulations and illustrated with data of a published meta-analysis. It is shown that the ML method has non-convergence issues or computational difficulties and at the same time allows estimation of the dependence between study-specific sensitivity and specificity and thus prediction via summary receiver operating curves.

Item Type:	Article
Uncontrolled Keywords:	copula mixed model,diagnostic odds ratio,generalized linear mixed model,specificity,sroc
Faculty \ School:	Faculty of Science > School of Computing Sciences
UEA Research Groups:	Faculty of Science > Research Groups > Data Science and AI Faculty of Science > Research Groups > Statistics (former - to 2024) Faculty of Science > Research Groups > Numerical Simulation, Statistics & Data Science
Related URLs:	https://arxiv.org/abs/1611.05344
Depositing User:	Pure Connector
Date Deposited:	23 Nov 2016 00:35
Last Modified:	07 Nov 2024 12:39
URI:	https://ueaeprints.uea.ac.uk/id/eprint/61421
DOI:	10.1007/s10182-017-0299-y

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