Cerullo et al MetaBayesDTA BMC 2023.pdf (3.45 MB)
MetaBayesDTA: codeless Bayesian meta-analysis of test accuracy, with or without a gold standard.
journal contributionposted on 2023-07-03, 10:44 authored by Enzo Cerullo, Alex J Sutton, Hayley E Jones, Olivia Wu, Terry J Quinn, Nicola J Cooper
BackgroundThe 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.
ResultsWe 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.
ConclusionsDue 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.
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)
Author affiliationDepartment of Population Health Sciences, University of Leicester
- VoR (Version of Record)