has mainly focused on a single test as acknowledged in a recent review by [@Ma-etal-2013].

However, as the understanding of a particular disease increases, along with technological advances, the development of multiple tests is apparent. Therefore, many studies that evaluate the accuracy of several tests instead of the accuracy of a single test have been conducted (e.g., @Nishimura-etal2007 [@Bennett-etal2007; @Kodama-etal2013]). 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 diagnostic studies which compared the accuracy of two tests .

[@trikalinos-etal-2014-rsm] were the first who developed a model for the joint meta-analysis of studies comparing two diagnostic tests. They proposed a multinomial generalized linear mixed model which assumes independent multinomial distributions for the counts of each combination of test results in diseased patients, and, the counts of each combination of test results in non-diseased patients, conditional on the transformed latent true positive rate (TPR) and false positive rate (FPR) for each test, and latent joint TPR and FPR, which capture information on the agreement between the two tests in each study. [@dimou-etal2016] extended the bivariate model of [@Reitsma-etal-2005], which 