MetaBayesDTA: codeless Bayesian meta-analysis of test accuracy, with or without a gold standard.
MetaBayesDTA: codeless Bayesian meta-analysis of test accuracy, with or without a gold standard.
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DOI：
10.1186/s12874-023-01910-y
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发表时间：
2023-05-25
影响因子：
4
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中科院分区：
文献类型：
--
作者：
为诊断测试准确性研究的荟萃分析开发的统计模型需要专业知识来实施。尤其是最近的指南，如科克伦诊断测试准确性系统评价手册第2版，提倡比以前更复杂的方法。本文介绍了一个基于Web的应用程序-MetaBayesDTA-，使许多先进的分析方法在这一领域更容易访问。我们使用R，Shiny包和Stan创建了这个应用程序。它允许基于双变量模型进行广泛的分析，包括亚组分析、荟萃回归和比较检验准确性评估的扩展。它还进行分析，而不是假设一个完美的参考标准，包括允许使用不同的参考测试。由于其用户友好性和广泛的功能，MetaBayesDTA应该吸引具有不同专业知识水平的研究人员。我们预计，该应用程序将鼓励更高水平的采用更先进的方法，最终应该提高测试准确性审查的质量。在线版本包含补充材料，可通过10.1186/s12874 - 023 - 01910-y获得。
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. 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. 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. The online version contains supplementary material available at 10.1186/s12874-023-01910-y.
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影响因子：
2
作者：
Menten, J.;Boelaert, M.;Lesaffre, E.
通讯作者：
Lesaffre, E.
影响因子：
4
作者：
Freeman, Suzanne C.;Kerby, Clareece R.;Sutton, Alex J.
通讯作者：
Sutton, Alex J.
影响因子：
6.9
作者：
JORM, AF;SCOTT, R;MACKINNON, AJ
通讯作者：
MACKINNON, AJ
影响因子：
9.8
作者：
Burke, Danielle L.;Ensor, Joie;Riley, Richard D.
通讯作者：
Riley, Richard D.
影响因子：
2
作者：
Barrett, Jessica K.;Farewell, Vern T.;Siannis, Fotios;Tierney, Jayne;Higgins, Julian P. T.
通讯作者：
Higgins, Julian P. T.