[This article was first published on Gianluca Baio's blog, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.
I think the newest release of BCEA, our R package to standardise and post-process the output of a health economic model, is now available from CRAN $-$ in fact, the source code is also available here. The package is rather stable, so the changes aren’t many, but the few ones are quite substantial, I think. In particular, we’ve now modified the function evppi, which is used to perform the analysis of the expected value of partial information (incidentally, that’s also related to our upcoming short course). Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.
In the last few years this has been a very interesting and fertile area of research within the health economics community, with interesting methods being proposed $-$ this is a nice editorial by Nicky Welton and Howard Thom, while this is (an arxived version of) our own technical review.
BCEA implements all the most recent methods, with particular focus on Strong et al‘s based on Gaussian Process regression and our own work (just published in Statistics in Medicine), which, building on their work, uses INLA to speed up the computation even further. In addition, we have also included a graphical tool that can be used to describe, at least as a first order approximation, the individual impact of each parameter on the overall uncertainty in the decision-making process. We have called this the info-rank plot, which is basically a generalisation of commonly used (especially when economic evaluations are performed under a frequentist approach) Tornado plots. The info-rank is based on the single-parameter EVPPI and can be used to roughly determine the contribution of each single parameters to the overall value of partial information (of course, because the EVPPI is a highly non-linear function, combinations of parameters are not additive, so some caution is needed here).
To leave a comment for the author, please follow the link and comment on their blog: Gianluca Baio's blog.
R-bloggers.com offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.