**Gianluca Baio's blog**, and kindly contributed to R-bloggers)

After months of work (although to be fair, we haven’t worked 100% full time on this), Andrea and I are nearly ready to publish the next release of BCEA.

Andrea has done a brilliant job and is responsible for most of the good new features *( NB: see what I’m doing here? Subtly putting the blame on him if things go tits up, but also appearing like a magnanimous supervisor who’s only pretending not to deserve full credit, if they don’t…)*.

But, seriously, I really mean that he’s been brilliant, especially since he had to put up with my being extremely picky on details like font size and similar! Anyway, we’re really excited (well, at least as excited as you can be about a computer package) about the new features, which basically are of three types.

- The first one is in terms of the graphical capabilities of the package. We have implemented all the graphical functions in ggplot2, which now complements the base graphical engine.
- The second one is that we have included the possibility of running multiple health economic comparisons in a single evaluation. In the current version of BCEA, you are allowed to have many interventions, but the comparisons are performed pairwise against one of them, which the user defines as the “reference” intervention. Now, it will be possible to produce an analysis of all the interventions jointly. This has clear links with multiple treatment comparisons (as pointed out in chapter 9 here).
- The third new feature allows the user to compute the expected value of partial information (EVPPI), with respect to one of the parameters included in the model. This is a very important aspect of the process of probabilistic sensitivity analysis and normally is performed using a two-stage MCMC process (which is explained in chapter 3 and 4 of BMHE). But this can be (and nearly always is) a very computationally intensive process. Also, you can’t use too few iterations in either of the two MCMC stages, because that has a crucial impact on the precision of the results. Also, it is difficult to standardise the analysis using the two-stage MCMC approach, because it depends very much on the model being fitted. However, Mohsen Sadatsafavi and colleagues have recently published a paper in which they found a clever way of approximating the EVPPI, once the original model has been run (ie with a single MCMC step, which you would do anyway). I wasn’t aware of the paper, but after Mohsen contacted me and pointed it out, I decided we should implement it in BCEA.

In the next couple of days we’ll release the new version as some sort of beta test. We have done some tests ourselves, of course, and everything seems to work OK. But of course it would be good if we could get more feedbacks on different problems.

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