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

A new interesting article has been published in Stat Methods Med Res. 2018 Sep 26:962280218796685. doi: 10.1177/0962280218796685. and titled:

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

Authors of this article are:
Nikoloulopoulos AK.

A summary of the article is shown below:
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.

Check out the article’s website on Pubmed for more information:



This article is a good source of information and a good way to become familiar with topics such as: Copula mixed model;SROC;generalised linear mixed model;sensitivity/specificity;vines.

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