MetaInsight is a new tool that is freely available and that conducts network meta-analysis (NMA) via the web requiring no specialist software for the user to install but leveraging established analysis routines (specifically the netmeta package in R). The tool is interactive and uses an intuitive ‘point and click’ interface and presents results in visually intuitive and appealing ways. It can also carry out sensitivity analysis on existing NMAs. It is hoped that this tool will assist those in conducting NMA who are not statistical experts, and, in turn, increase the relevance of published meta-analyses, and in the long term contribute to improved healthcare decision making as a result.
This app provides a user friendly (“point and click”) web interface for conducting meta-analysis of diagnostic test accuracy (DTA) studies. It uses macros written (by others) in R “behind the scenes” to achieve this. Analysis options include the bivariate meta-analysis model - recommended for use in Cochrane DTA reviews (but not available in Cochrane software). It can also be used to carry out sensitivity analysis on existing meta-analyses. All results produced are downloadable, including high quality graphical plots. It is hoped that this tool will assist those in conducting NMA who are not statistical experts, and, in turn, increase the relevance of published meta-analyses, and in the long term contribute to improved healthcare decision making as a result.
If you use this app please cite it as:
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://doi.org/10.1186/s12874-019-0724-x
This interactive explorable explanation is designed to teach the basics of diagnostic test accuracy evaluation and is recommended for anyone new to the area (including those planning to move into diagnostic test synthesis) or those who never quite got their head around an ROC curve or needs a bit of revision on the topic.
A smoother implementation of the core idea in the above is now available here (Note: You will probably still want to look at the initial app for the explanations and extra content etc)