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Activity Number: 239
Type: Topic Contributed
Date/Time: Monday, August 1, 2016 : 2:00 PM to 3:50 PM
Sponsor: Health Policy Statistics Section
Abstract #319002
Title: A Bayesian HSROC Model for Meta-Analysis of Multiple Diagnostic Tests
Author(s): Qinshu Lian* and Haitao Chu
Companies: University of Minnesota and University of Minnesota
Keywords: Bayesian hierarchical model ; Diagnostic tests ; Missing data ; Multiple tests comparison ; Network meta-analysis

In studies evaluating the accuracy of diagnostic tests, three designs are commonly used: (1) the crossover design; (2) the randomized design; and (3) the non-comparative design. Existing methods on meta-analysis of diagnostic tests mainly considered the simple cases when the reference test in all or none of the studies can be considered as a gold standard test, and when all studies use either a randomized or non-comparative design. Yet the proliferation of diagnostic instruments and diversity of study designs being used have boosted the demand to develop more general methods to combine studies with or without a gold standard test using different designs. In this paper, we extend the Bayesian hierarchical summary receiver operating characteristic model to network meta-analysis of diagnostic tests to simultaneously compare multiple tests under a missing data framework. It accounts for the potential correlations between multiple tests within a study and the heterogeneity across studies. In addition, it allows different studies to perform different subsets of diagnostic tests. Our model is evaluated through simulations and illustrated using real data from deep vein thrombosis tests.

Authors who are presenting talks have a * after their name.

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