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Meta-analysis of diagnostic accuracy studies with multiple thresholds: Comparison of different approaches

  • Methods for standard meta-analysis of diagnostic test accuracy studies are well established and understood. For the more complex case in which studies report test accuracy across multiple thresholds, several approaches have recently been proposed. These are based on similar ideas, but make different assumptions. In this article, we apply four different approaches to data from a recent systematic review in the area of nephrology and compare the results. The four approaches use: a linear mixed effects model, a Bayesian multinomial random effects model, a time-to-event model and a nonparametric model, respectively. In the case study data, the accuracy of neutrophil gelatinase-associated lipocalin for the diagnosis of acute kidney injury was assessed in different scenarios, with sensitivity and specificity estimates available for three thresholds in each primary study. All approaches led to plausible and mostly similar summary results. However, we found considerable differences in results for some scenarios, for example, differences in the area under the receiver operating characteristic curve (AUC) of up to 0.13. The Bayesian approach tended to lead to the highest values of the AUC, and the nonparametric approach tended to produce the lowest values across the different scenarios. Though we recommend using these approaches, our findings motivate the need for a simulation study to explore optimal choice of method in various scenarios.

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Author:Antonia Zapf, Christian Albert, Cornelia FrömkeGND, Michael Haase, Annika Hoyer, Hayley E. Jones, Gerta Rücker
DOI original:https://doi.org/10.1002/bimj.202000091
Parent Title (English):Biometrical Journal
Document Type:Article
Year of Completion:2021
Publishing Institution:Hochschule Hannover
Release Date:2021/02/11
Tag:bivariate endpoint; meta-analysis; multiple thresholds; sensitivity; specificity
GND Keyword:Metaanalyse; Diagnostik; Genauigkeit; Methode; Vergleich
First Page:1
Last Page:13
Link to catalogue:1757220747
Institutes:Fakultät III - Medien, Information und Design
DDC classes:510 Mathematik
610 Medizin, Gesundheit
Licence (German):License LogoCreative Commons - CC BY-NC-ND - Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International