A Bayesian Hierarchical Model for Network Meta-Analysis of Diagnostic Tests

• Activity Number: 253
• Type: Contributed
• Date/Time: Monday, August 10, 2015 : 2:00 PM to 3:50 PM
• Sponsor: Biometrics Section
• Author(s): Xiaoye Ma* and Haitao Chu and Yong Chen and Joseph Ibrahim
• Companies: University of Minnesota and University of Minnesota, Twin Cities and The University of Texas School of Public Health and The University of North Carolina
• Keywords: diagnostic tests ; hierarchical models ; inconsistency ; missing data ; multiple test comparison ; network meta-analysis

Abstract

To compare the accuracy of multiple tests in a single study, three designs are commonly used: 1) the multiple test comparison design; 2) the randomized design and 3) 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. We develop a missing data framework and a Bayesian hierarchical model for network MA-DT and offer important promises: 1) it combines studies using all three designs; 2) it pools both studies with or without a gold standard; 3) it combines studies with different sets of candidate tests; and 4) it accounts for heterogeneity across studies and complex correlation structure among multiple tests. We illustrate our method through a case study: network MA-DT of deep vein thrombosis tests. Finally, we evaluate the performance of the proposed method through simulation studies.