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

TL;DR

This paper introduces a D-vine copula mixed model for joint meta-analysis of two diagnostic tests, offering improved fit and detailed comparison of test performance, including SROC parameters and tail dependencies.

Contribution

The paper presents a flexible D-vine copula mixed model that generalizes the quadrivariate GLMM for diagnostic test meta-analysis, capturing tail dependencies and operating on original data scales.

Findings

Model outperforms GLMM in data fit

Allows direct calculation of sensitivities and specificities

Provides detailed SROC curve comparison

Abstract

For a particular disease there may be two diagnostic tests developed, where each of the tests is subject to several studies. A quadrivariate generalized 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 2 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.