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MetaBayesDTA: Codeless Bayesian meta-analysis of test accuracy, with or without a gold standard by Cerullo, Enzo; Sutton, Alex J.; Jones, Hayley E.; Wu, Olivia; Quinn, Terry J.; Cooper, Nicola J. is a scholarly article available to read on EtoBox.

What is MetaBayesDTA: Codeless Bayesian meta-analysis of test accuracy, with or without a gold standard about?

Introduction: Despite their applicability, statistical models used for the meta-analysis of test accuracy require specialised knowledge to implement, with the necessary level of expertise having recently increased. This is due to the development and recommendation to use more sophisticated methods; such as those in Version 2 of the Cochrane Handbook for Systematic Reviews of Diagnostic Test Accuracy. This paper describes a web-based application that extends the functionality of previous applications, making many advanced analysis methods more accessible. Methods: We sought to create an extended, stand-alone, Bayesian version of MetaDTA, which (i) has the benefits of previously proposed applications and addresses key limitations of them, (ii) is accessible to researchers who do not have the specific expertise required to fit such models, and (iii) is suitable for experienced analysts. We created the application using Shiny and Stan. Results: We created MetaBayesDTA (https://crsu.shinyapps.io/MetaBayesDTA/), which allows users to conduct meta-analysis of test accuracy, with or without a gold standard. The application addresses several key limitations of other applications. For instan

Author

Cerullo, Enzo; Sutton, Alex J.; Jones, Hayley E.; Wu, Olivia; Quinn, Terry J.; Cooper, Nicola J.

Published

2022

Language

EN