MetaBayesDTA: codeless Bayesian meta-analysis of test accuracy, with or without a gold standard

Cerullo, E., Sutton, A. J., Jones, H. E., Wu, O. , Quinn, T. J. and Cooper, N. J. (2023) MetaBayesDTA: codeless Bayesian meta-analysis of test accuracy, with or without a gold standard. BMC Medical Research Methodology, 23, 127. (doi: 10.1186/s12874-023-01910-y) (PMID:37231347) (PMCID:PMC10210277)

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Abstract

Background: The statistical models developed for meta-analysis of diagnostic test accuracy studies require specialised knowledge to implement. This is especially true since recent guidelines, such as those in Version 2 of the Cochrane Handbook of Systematic Reviews of Diagnostic Test Accuracy, advocate more sophisticated methods than previously. This paper describes a web-based application - MetaBayesDTA - that makes many advanced analysis methods in this area more accessible. Results: We created the app using R, the Shiny package and Stan. It allows for a broad array of analyses based on the bivariate model including extensions for subgroup analysis, meta-regression and comparative test accuracy evaluation. It also conducts analyses not assuming a perfect reference standard, including allowing for the use of different reference tests. Conclusions: Due to its user-friendliness and broad array of features, MetaBayesDTA should appeal to researchers with varying levels of expertise. We anticipate that the application will encourage higher levels of uptake of more advanced methods, which ultimately should improve the quality of test accuracy reviews.

Item Type:Articles
Additional Information:The work was carried out whilst EC was funded by a National Institute for Health Research (NIHR) Complex Reviews Support Unit (project number 14/178/29) and by an NIHR doctoral research fellowship (project number NIHR302333).
Keywords:Diagnostic test accuracy, imperfect gold standard, application, latent class, meta-Analysis.
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Quinn, Professor Terry and Wu, Professor Olivia
Authors: Cerullo, E., Sutton, A. J., Jones, H. E., Wu, O., Quinn, T. J., and Cooper, N. J.
College/School:College of Medical Veterinary and Life Sciences > School of Cardiovascular & Metabolic Health
College of Medical Veterinary and Life Sciences > School of Health & Wellbeing > Health Economics and Health Technology Assessment
Journal Name:BMC Medical Research Methodology
Publisher:BioMed Central
ISSN:1471-2288
ISSN (Online):1471-2288
Copyright Holders:Copyright © 2023 The Authors
First Published:First published in BMC Medical Research Methodology 23: 127
Publisher Policy:Reproduced under a Creative Commons License

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
190914SRP Complex Reviews Research Support UnitOlivia WuNational Institute for Health Research (NIHR)14/178/29HW - Health Economics and Health Technology Assessment