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

Xiaoye Ma, Qinshu Lian, Haitao Chu, Joseph G. Ibrahim, Yong Chen

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)87-102
Number of pages16
JournalBiostatistics
Volume19
Issue number1
DOIs
StatePublished - Jan 1 2018

Bibliographical note

Funding Information:
Research reported in this publication was supported in part by NIAID R21 AI103012 (H.C., X.M.), NIDCR R03 DE024750 (H.C.), NLM R21 LM012197 (H.C.), NIDDK U01 DK106786 (H.C.), and NHLBI T32HL129956 (Q.L). The content is solely the responsibility of the authors and does not necessarily represent official views of the National Institutes of Health.

Keywords

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

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