## Monday, 2 December 2013

Very shortly, I'll upload the newest release of BCEA, my R package to post-process the output of a (Bayesian) health economic model and produce systematic summaries (such as graphs and tables) for a full economic evaluation and probabilistic sensitivity analysis (more posts on this are, in random order, here, here, here and here).

I've made changes of two kinds: the first one can go under the header of "cosmetic changes" (like Pietro Rigo, who taught me a couple of courses back in my undergraduate studies, used to say). Basically, I decided to print out the whole script for BCEA and while I was going through it I realised that in several points I wasn't really being very elegant or particularly effective with my code. This was not a huge problem, I think, because speed of execution is generally not an issue $-$ the inputs to the functions are usually moderately small and the operations required are not too complicated. But, once I realised that, it bothered me that my code was full of loops and unnecessary lines and so I changed it. As it happens, the gains in terms of computational speed are not huge (because of the reason I mentioned earlier). But the cockroach lemma still applies...

The second type of changes could be labelled as "substantial" and involve the following.
1. I've included a function multi.evppi, which implements the method for multivariate analysis of the expected value of partial perfect information, described here. (The next bit of information is totally irrelevant, but when I went to Edinburgh for the MRC clinical trial conference, I caught up with Mark Strong, who gave a talk on this, which motivated me to finish off the script).
2. A utility function called CreateInputs, which can be called to produce the object containing the matrix with the parameters simulated using the MCMC procedure (using JAGS or BUGS) and a vector of parameters (as strings) that can be used to perform the EVPPI analysis.
3. I've also coded up (borrowing from Mark's original scripts) a function to perform diagnostic analysis on the assumptions underlying the Gaussian process model that is used to estimate the EVPPI. That's called diag.evppi.
4. A function that performs structural probabilistic sensitivity analysis, which is called struct.psa. I think this was long due, since basically most of the inputs were already there $-$ you only have to run the model using different specifications and save (some of) the results to a list and then this function will compute the weights to be associated with each model specification (according to the methods specified in this paper by Chris Jackson et al.
This will be version 2.0-2 $-$ I've toyed with the idea of moving up a gear in the numbering, since it seems to me to be a substantial improvement (more for the inclusion of the new functions than anything else). But I also thought that it would be good to have it out there and see what people think of it and possibly test it (I know that some people are using BCEA so hopefully we'll get some feedback in time for our book).