App link: https://crsu.shinyapps.io/MetaBayesDTA/
Paper link: https://bmcmedresmethodol.biomedcentral.com/articles/10.1186/s12874-023-01910-y
MetaBayesDTA is an extended, Bayesian version of MetaDTA (https://crsu.shinyapps.io/dta_ma/), which allows users to conduct meta-analysis of test accuracy, with or without assuming a gold standard. Due to its user-friendliness and broad array of features, MetaBayesDTA should appeal to a wide variety of applied researchers, including those who do not have the specific expertise required to fit such models using statistical software. Furthermore, MetaBayesDTA has many features not available in other apps. For instance, for the bivariate model, one can conduct subgroup analysis and univariate meta-regression. Meanwhile, for the model which does not assume a perfect gold standard, the app can partially account for the fact that different studies in a meta-analysis often use different reference tests using meta-regression.
Please note that the current release does not yet have a manual. Please send questions and any feedback - including bug reports and suggestions for new features - to the CRSU Team at [email protected].
We kindly ask you to cite this app as:
Cerullo, E., Sutton, A.J., Jones, H.E. et al. MetaBayesDTA: codeless Bayesian meta-analysis of test accuracy, with or without a gold standard. BMC Med Res Methodol 23, 127 (2023). https://doi.org/10.1186/s12874-023-01910-y
Furthermore, please cite the following papers associated with the previous version of the app - MetaDTA - whenever outputs from MetaBayesDTA are used:
Patel A, Cooper NJ, Freeman SC, Sutton AJ. Graphical enhancements to summary receiver operating charcateristic plots to facilitate the analysis and reporting of meta-analysis of diagnostic test accuracy data. Research Synthesis Methods 2020, https://doi.org/10.1002/jrsm.1439
Freeman SC, Kerby CR, Patel A, Cooper NJ, Quinn T, Sutton AJ. Development of an interactive web-based tool to conduct and interrogate meta-analysis of diagnostic test accuracy studies: MetaDTA. BMC Medical Research Methodology 2019; 19: 81, https://bmcmedresmethodol.biomedcentral.com/articles/10.1186/s12874-019-0724-x