A Bayesian Hierarchical Summary Receiver Operating Characteristic Model for Network Meta-Analysis of Diagnostic Tests

ABSTRACT

In studies evaluating the accuracy of diagnostic tests, three designs are commonly used, crossover, randomized, and noncomparative. Existing methods for meta-analysis of diagnostic tests mainly consider the simple cases in which the reference test in all or none of the studies can be considered a gold standard test, and in which all studies use either a randomized or noncomparative design. The proliferation of diagnostic instruments and the diversity of study designs create a need for more general methods to combine studies that include or do not include a gold standard test and that use various designs. This article extends the Bayesian hierarchical summary receiver operating characteristic model to network meta-analysis of diagnostic tests to simultaneously compare multiple tests within a missing data framework. The method accounts for correlations between multiple tests and for heterogeneity between studies. It also allows different studies to include different subsets of diagnostic tests and provides flexibility in the choice of summary statistics. The model is evaluated using simulations and illustrated using real data on tests for deep vein thrombosis, with sensitivity analyses. Supplementary materials for this article are available online.

KEYWORDS:

• Bayesian hierarchical model
• Diagnostic tests
• Missing data
• Multiple tests comparison
• Network meta-analysis

Supplementary Materials

The supplemental materials for this article contain 4 sections. Appendix A provides justification of the likelihood under MAR. Appendix B provides more details on the case study, including data description, priors for all models and additional case study results. Appendix C provides more details on the simulation studies. Appendix D gives the derivation of population average estimates.

Acknowledgment

The authors thank the editor Professor Joseph G. Ibrahim, an associate editor, and two anonymous reviewers for many constructive comments. Conflict of Interest: None declared.

Additional information

Funding

Research reported in this publication was supported in part by NIDCR R03 DE024750 (H.C.), NLM R21 LM012197 (H.C.), NLM R21 LM012744 (H.C., J.H.), NIDDK U01 DK106786 (H.C.), AHRQ R03HS024743 (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.