1-truncated C-vine copula mixed models for network meta-analysis of multiple diagnostic tests

TL;DR

This paper introduces 1-truncated C-vine copula mixed models as a flexible alternative to GLMMs for network meta-analysis of multiple diagnostic tests, capturing complex dependencies and improving inference.

Contribution

The paper proposes a novel 1-truncated C-vine copula mixed model framework that generalizes GLMMs for better modeling of dependencies in diagnostic test meta-analysis.

Findings

Models outperform GLMMs in capturing tail dependencies.

Simulation studies demonstrate improved accuracy.

Case study shows practical applicability and benefits.

Abstract

As meta-analysis of multiple diagnostic tests impacts clinical decision making and patient health, there is growing interest in statistical models that synthesize evidence from studies comparing multiple diagnostic tests. To compare the accuracy of multiple diagnostic tests in a single study, three designs are commonly used: (i) the multiple test comparison design; (ii) the randomized design, and (iii) the non-comparative design. Generalized linear mixed models (GLMMs) are currently the recommended approach for jointly meta-analyzing data from all three designs, enabling simultaneous inference. In this context, 1-truncated C-vine copula mixed models are proposed as a flexible and powerful alternative. These models generalize the GLMM framework by allowing for arbitrary univariate distributions of the random effects and capturing tail dependencies and asymmetries. We demonstrate the utility of our methods with an extensive simulation study and by insightfully re-analysing a case study on the network meta-analysis of diagnostic tests for deep vein thrombosis. Findings indicate that 1-truncated C-vine copula mixed models can offer improvements over GLMMs, supporting their adoption for network meta-analysis of multiple diagnostic tests.