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Diagnostic test accuracy studies observe the result of a gold standardprocedure that defines the presence or absence of a disease and the result of adiagnostic test. They typically report the number of true positives, falsepositives, true negatives and false negatives. However, diagnostic testoutcomes can also be either non-evaluable positives or non-evaluable negatives.We propose a novel model for meta-analysis of diagnostic studies in thepresence of non-evaluable outcomes that assumes independent multinomialdistributions for the true and non-evaluable positives, and, the true and nonevaluable negatives, conditional on the latent sensitivity, specificity,probability of non-evaluable positives and probability of non-evaluablenegatives in each study. For the random effects distribution of the latentproportions, we employ a drawable vine copula that can successively model thedependence in the joint tails. Our methodology is demonstrated with anextensive simulation study and applied to data from diagnostic accuracy studiesof coronary computed tomography angiography for the detection of coronaryartery disease. The comparison of our method with the existing approachesyields findings in the real data application that change the currentconclusions.

A multinomial quadrivariate D-vine copula mixed model for meta-analysis of diagnostic studies in the presence of non-evaluable subjects