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There is an extensive literature on methods for meta-analysis of diagnosticstudies, but it mainly focuses on a single test. However, the betterunderstanding of a particular disease has led to the development of multipletests. A multinomial generalized linear mixed model (GLMM) is recently proposedfor the joint meta-analysis of studies comparing multiple tests. We propose anovel model for the joint meta-analysis of multiple tests, which assumesindependent multinomial distributions for the counts of each combination oftest results in diseased and non-diseased patients, conditional on the latentvector of probabilities of each combination of test results in diseased andnon-diseased patients. For the random effects distribution of the latentproportions, we employ a truncated drawable vine copula that can cover flexibledependence structures. The proposed model includes the multinomial GLMM as aspecial case, but can also operate on the original scale of the latentproportions. Our methodology is demonstrated with a simulation study and usinga meta-analysis of screening for Down syndrome with two tests: shortenedhumerus and shortened femur. The comparison of our method with the multinomialGLMM yields findings in the real data meta-analysis that change the currentconclusions.

A multinomial truncated D-vine copula mixed model for the joint meta-analysis of multiple diagnostic tests