Diagnostic test accuracy network meta-analysis methods: A scoping review and empirical assessment

Areti Angeliki Veroniki; Sofia Tsokani; Ridhi Agarwal; Eirini Pagkalidou; Gerta Rücker; Dimitris Mavridis; Yemisi Takwoingi

doi:10.1016/j.jclinepi.2022.02.001

Diagnostic test accuracy network meta-analysis methods: A scoping review and empirical assessment

J Clin Epidemiol. 2022 Jun:146:86-96. doi: 10.1016/j.jclinepi.2022.02.001. Epub 2022 Feb 16.

Authors

Areti Angeliki Veroniki¹, Sofia Tsokani², Ridhi Agarwal³, Eirini Pagkalidou⁴, Gerta Rücker⁵, Dimitris Mavridis⁶, Yemisi Takwoingi⁷

Affiliations

¹ Knowledge Translation Program, Li Ka Shing Knowledge Institute, St. Michael's Hospital, 209 Victoria Street, East Building, Toronto, Ontario M5B 1T8, Canada; Institute of Health Policy Management and Evaluation, University of Toronto, Toronto, Ontario, Canada. Electronic address: areti-angeliki.veroniki@unityhealth.to.
² Department of Primary Education, School of Education, University of Ioannina, Ioannina, Greece.
³ Test Evaluation Research Group, Institute of Applied Health Research, University of Birmingham, Birmingham, UK.
⁴ Department of Hygiene, Social-Preventive Medicine and Medical Statistics, Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece.
⁵ Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center - University of Freiburg, Stefan-Meier-Strasse 26, 79104 Freiburg, Germany.
⁶ Department of Primary Education, School of Education, University of Ioannina, Ioannina, Greece; Paris Descartes University, Sorbonne Paris Cité, Faculté de Médecine, Paris, France.
⁷ Test Evaluation Research Group, Institute of Applied Health Research, University of Birmingham, Birmingham, UK; NIHR Birmingham Biomedical Research Centre, University Hospitals Birmingham NHS Foundation Trust and University of Birmingham, Birmingham, UK.

PMID: 35181490
DOI: 10.1016/j.jclinepi.2022.02.001

Abstract

Objectives: To (a) identify methodological and application papers reporting a model developed specifically for diagnostic test accuracy network meta-analysis (DTA-NMA) or a hierarchical meta-regression method for comparing at least three index tests; (b) review and summarize the characteristics of the methods and the application papers; and (c) compare DTA-NMA and hierarchical meta-regression methods empirically.

Study design and setting: We performed a scoping review and searched major databases until March 3rd, 2021. We assessed the characteristics of the identified methods, conducted a descriptive analysis of characteristics of the application articles, and applied the DTA-NMA and meta-regression methods to the available data.

Results: We included 49 articles (plus one companion report), of which nine were methodological (describing 11 DTA-NMA methods) and 40 were application papers (data available for 32 DTA-NMAs). Our results showed a steep increase in recent years in DTA-NMA publications. DTA-NMA models may lead to different results. Although sensitivity estimates were comparable between meta-regression and DTA-NMA models, specificity estimates were higher in meta-regression.

Conclusion: The choice of a DTA-NMA model will depend on the available data, including the use of different thresholds for test positivity, different study designs, and software familiarity. Selection between the methods may impact on the NMA results, especially for specificity.

Keywords: Accuracy; Diagnostic test; Indirect comparison; Network meta-analysis; Sensitivity; Specificity.

Publication types

Review
Research Support, Non-U.S. Gov't

MeSH terms

Diagnostic Tests, Routine*
Humans
Network Meta-Analysis
Regression Analysis
Research Design
Research Report*