A D-vine copula mixed model for joint meta-analysis and comparison of diagnostic tests

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For a particular disease, there may be two diagnostic tests developed, where each of the tests is subject to several studies. A quadrivariate generalised linear mixed model (GLMM) has been recently proposed to joint meta-analyse and compare two diagnostic tests. We propose a D-vine copula mixed model for joint meta-analysis and comparison of two diagnostic tests. Our general model includes the quadrivariate GLMM as a special case and can also operate on the original scale of sensitivities and specificities. The method allows the direct calculation of sensitivity and specificity for each test, as well as the parameters of the summary receiver operator characteristic (SROC) curve, along with a comparison between the SROCs of each test. Our methodology is demonstrated with an extensive simulation study and illustrated by meta-analysing two examples where two tests for the diagnosis of a particular disease are compared. Our study suggests that there can be an improvement on GLMM in fit to data since our model can also provide tail dependencies and asymmetries.

Original languageEnglish
Pages (from-to)3286-3300
Number of pages15
JournalStatistical Methods in Medical Research
Issue number10-11
Early online date26 Sep 2018
Publication statusPublished - 1 Oct 2019


  • Copula mixed model
  • generalized linear mixed model
  • sensitivity/specificity
  • SROC
  • vines

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