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Despite their applicability, statistical models used for themeta-analysis of test accuracy require specialised knowledge to implement, withthe necessary level of expertise having recently increased. This is due to thedevelopment and recommendation to use more sophisticated methods; such as thosein Version 2 of the Cochrane Handbook for Systematic Reviews of Diagnostic TestAccuracy. This paper describes a web-based application that extends thefunctionality of previous applications, making many advanced analysis methodsmore accessible.  Methods: We sought to create an extended, stand-alone, Bayesian version ofMetaDTA, which (i) has the benefits of previously proposed applications andaddresses key limitations of them, (ii) is accessible to researchers who do nothave the specific expertise required to fit such models, and (iii) is suitablefor 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 agold standard. The application addresses several key limitations of otherapplications. For instance, for the bivariate model, one can conduct subgroupanalysis, univariate meta-regression, and comparative test accuracy evaluation.Meanwhile, for the model which does not assume a perfect gold standard, theapplication can account for the fact that studies use different referencetests.  Conclusions: Due to its user-friendliness and broad array of features,MetaBayesDTA should appeal to a wide variety of researchers. We anticipate thatthe application will encourage wider use of more advanced methods, whichultimately should improve the quality of test accuracy reviews.

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