An one-factor copula mixed model for joint meta-analysis of multiple diagnostic tests

TL;DR

This paper introduces a one-factor copula mixed model for joint meta-analysis of multiple diagnostic tests, reducing computational complexity and enabling effective comparison of three or more tests in clinical studies.

Contribution

The paper proposes a novel one-factor copula model that simplifies the meta-analysis of multiple diagnostic tests by reducing the dimensionality of the likelihood calculation.

Findings

Model effectively handles three or more tests without high-dimensional integration

Simulation studies validate the model's accuracy and efficiency

Application identifies the best diagnostic test for rheumatoid arthritis

Abstract

As the meta-analysis of more than one diagnostic tests can impact clinical decision making and patient health, there is an increasing body of research in models and methods for meta-analysis of studies comparing multiple diagnostic tests. The application of the existing models to compare the accuracy of three or more tests suffers from the curse of multi-dimensionality, i.e., either the number of model parameters increase rapidly or high dimensional integration is required. To overcome these issues in joint meta-analysis of studies comparing $T >2$ diagnostic tests in a multiple tests design with a gold standard, we propose a model that assumes the true positives and true negatives for each test are conditionally independent and binomially distributed given the $2T$-variate latent vector of sensitivities and specificities. For the random effects distribution, we employ an one-factor copula that provides tail dependence or tail asymmetry. Maximum likelihood estimation of the model is straightforward as the derivation of the likelihood requires bi-dimensional instead of $2T$-dimensional integration. Our methodology is demonstrated with an extensive simulation study and an application example that determines which is the best test for the diagnosis of rheumatoid arthritis.