Challenges in Comparative Meta-Analysis of the Accuracy of Multiple Diagnostic Tests.
Diagnostic test
Indirect comparison
Network meta-analysis
Sensitivity
Specificity
Systematic review
Test accuracy
Journal
Methods in molecular biology (Clifton, N.J.)
ISSN: 1940-6029
Titre abrégé: Methods Mol Biol
Pays: United States
ID NLM: 9214969
Informations de publication
Date de publication:
2022
2022
Historique:
entrez:
22
9
2021
pubmed:
23
9
2021
medline:
8
1
2022
Statut:
ppublish
Résumé
The rapid increase in diagnostic and screening techniques has urged the need to choose among multiple diagnostic tests. For the majority of diseases, there is more than a single test available, and studies usually compare a subset of these tests. In such cases, a separate meta-analysis of each test cannot provide a reliable answer on the relative accuracy of the multiple available tests. Extensions of standard (hierarchical) meta-analysis to network meta-analysis (NMA) models for the comparison of at least three diagnostic tests have been the subject of methodological research in recent years. NMA can be used to jointly analyze the totality of evidence in order to provide estimates of relative accuracy (sensitivity and specificity ), to compare tests that have not been compared head-to-head, and to obtain a ranking of all competing tests in order to further facilitate the decision-making process.In this chapter, we illustrate current methodology for meta-analysis of multiple test comparisons, introduce NMA methods of diagnostic tests as an extension to the standard meta-analysis of diagnostic test accuracy (DTA) studies, and present existing approaches to rank tests according to their accuracy, specificity , and sensitivity . We also describe the basic concepts, underlying assumptions, and challenges in NMA of multiple diagnostic tests.
Identifiants
pubmed: 34550598
doi: 10.1007/978-1-0716-1566-9_18
doi:
Types de publication
Comparative Study
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
299-316Subventions
Organisme : Department of Health
Pays : United Kingdom
Informations de copyright
© 2022. Springer Science+Business Media, LLC, part of Springer Nature.
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