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Biostatistics. 2018 Jan 1;19(1):87-102. doi: 10.1093/biostatistics/kxx025.

A Bayesian hierarchical model for network meta-analysis of multiple diagnostic tests.

Author information

Division of Biostatistics, School of Public Health, University of Minnesota, 420 Delaware St, Minneapolis, MN 55455, USA or
Department of Biostatistic, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, 135 Dauer Dr, Chapel Hill, NC 27599, USA.
Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania 210 Blockley Hall, 423 Guardian Drive, Philadelphia, PA 19104-6021, USA.


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. Existing meta-analysis methods of diagnostic tests (MA-DT) have been focused on evaluating the performance of a single test by comparing it with a reference test. The increasing number of available diagnostic instruments for a disease condition and the different study designs being used have generated the need to develop efficient and flexible meta-analysis framework to combine all designs for simultaneous inference. In this article, we develop a missing data framework and a Bayesian hierarchical model for network MA-DT (NMA-DT) and offer important promises over traditional MA-DT: (i) It combines studies using all three designs; (ii) It pools both studies with or without a gold standard; (iii) it combines studies with different sets of candidate tests; and (iv) it accounts for heterogeneity across studies and complex correlation structure among multiple tests. We illustrate our method through a case study: network meta-analysis of deep vein thrombosis tests.


Diagnostic test; Hierarchical model; Missing data; Multiple test comparison; Network meta-analysis

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