I was really impressed by the very smooth process through which my paper on cost-effectiveness analysis in the presence of structural zero costs (which I've already mentioned for example here and here; the related R package is described here) has gone in Statistics in Medicine.
The review process was quick and helpful (at least for one of the referees and the editor $-$ the other referee was kind of not very helpful at all). More importantly, thanks to a recent agreement that UCL have signed with various publishers, the paper can be published in open access $-$ the final version is freely available here.
The only thing I'm not quite completely sure I'm happy about is this: like many journals, SiM has a LaTeX template that you can use while preparing your manuscript. The draft version looks quite neat. But then, when they produce the final version, the maths fonts look more like those obtained using an old version of MS Word. Now, I can see that not everybody uses LaTeX, but once they go to the trouble of re-typesetting the accepted papers anyway, I don't see why they don't do it in LaTeX (which would look much better!).
But I may be missing some trivial point here. In fact, I was talking to somebody from Wiley earlier today, but I forgot to ask...
By pure accident (honest! I didn't do it on purpose!), last week I opened a package that had come in the post for Marta. Too bad that, of all the packages I could have opened, this was the Christmas gift she had bought (in fact I should say manufactured) for me.
There was no point in pretending I didn't know what it was and so we decided I should open it. Now, the question is: who is the geekiest in the family? Is it me for loooving the present, or Marta for even conceiving it?
This morning I heard from Christian the news that Dennis Lindley has passed away, last Saturday. He was 90. I think I'd already linked to this video in a different post, but I'm re-linking it right now.
OK, so first things first. I've always:
1) been a huge fan of the reindeer hat;
2) wondered what George Michael had actually given as a present to the other guy (if anything!).
On a much less serious note, as I managed to get (nearly completely) out of teaching for Term 1, I've been able to spend a bit more time on preparing and giving talks this year, particularly since September. On Sunday, I'll present the paper on Bayesian cost-effectiveness analysis for structural zero costs (a preliminary version of the full paper is arxived here, but the paper has also been accepted for publication in Statistics in Medicine and will be open-access shortly $-$ I think I'll talk about this in a separate post) at the 6th International Conference of the ERCIM WG on Computational and Methodological Statistics.
This will be my first time at ERCIM, so I'm not sure what to expect $-$ the session (ES19, room B33) looks quite interesting, although topic of my talk is probably a bit at odds with the others. Anyway, I've given this talk already a few times and for different audiences (eg here) but this time it will be a bit different, because of the changes to the model that I've made in response to the referees' comments and also because of the much shorter time $-$ only 15 minutes this time.
This is the logo for the 2014 edition of Bayes Pharma. Very soon we'll advertise the programme and more details $-$ I'll post about it as well.
At the moment, we're finalising the plan but I think we've decided to structure the workshops in this way. As a general rule, we consider three main areas for invited talks. These are:
1) Bayesian methods in clinical trials
2) Advanced Bayesian modelling
3) Bayesian methods for exploratory and epidemiological analysis
Each year, we could find something specific to fit under these (very loose) headers. For 2014, these are the proposed topics:
1) Power prior vs hierarchical modelling
2) Structural Equation Models
3) Health economic evaluation
(this last topic is not surprising $-$ although it was Emmanuel to suggest it, not me!).
Of course, In addition to these we'll have the contributed session (we'll ask people to submit their abstract).
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.
- 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).
- 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.
- 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.
- 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).
