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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

1
Division of Biostatistics, School of Public Health, University of Minnesota, 420 Delaware St, Minneapolis, MN 55455, USA xiaoye1043@gmail.com or chux0051@umn.edu.
2
Department of Biostatistic, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, 135 Dauer Dr, Chapel Hill, NC 27599, USA.
3
Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania 210 Blockley Hall, 423 Guardian Drive, Philadelphia, PA 19104-6021, USA.

Abstract

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.

KEYWORDS:

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

PMID:
28586407
PMCID:
PMC6454495
DOI:
10.1093/biostatistics/kxx025
[Indexed for MEDLINE]
Free PMC Article

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