**Gianluca Baio's blog**, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)

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During the summer, we’ve worked silently but relentlessly to set up a departmental server that could run R-Shiny applications.

There’s a bunch of us in the department doing work on R and producing packages and so we thought it’d be a good idea to disseminate our research. Which is just as well, as I’ve been nominated “*2020 REF Impact Czar*“, meaning I’ll have to help collate all the evidence that our work does have an impact on the “real world”…

Anyway, after some teething problems (mainly due to my getting familiar with the system and the remote installation of R and Shiny), I think we’ve now managed to successfully “deploy” (I think that’s the correct technical term) two webapps.

These are bmetaweb and BCEAweb. The first one is the web-interface to our bmeta package for Bayesian meta-analysis (which I developed with my PhD student Christina). The main point of bmeta is to allow some sort of standardised framework for a set of models for meta-analysis, depending on the nature of the outcome and some modelling assumption (eg fixed vs random effects). In addition to running the default models (which are based on rather vague priors and pre-defined model structures), bmeta saves data and model code (in JAGS), so that people can actually use these templates and actually modify them to their specific needs.

BCEAweb is the actual mother of the whole project (much as SAVI is then the actual grandmother, as it inspired our work on developing web-interfaces to R packages) and the idea is to use remotely BCEA to post-process the outcome of a (Bayesian) health economic model. BCEAweb works by uploading the simulations from a model and then using remotely R to produce all the relevant output for the reporting of the results in terms of cost-effectiveness analysis.

One thing we’ve tried very hard to include in both the webapps is the possibility of downloading a full report (in .pdf or .docx format) with a summary of the analyses. I think this is really cool and we’ll probably develop more of these $-$ particularly for our work related to statistical methods for health economic evaluations.

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