## Tuesday, 21 February 2017

### Coming soon!

We've just received a picture of the cover of the BCEA book, which is really, really close to being finally published!

I did mention this in a few other posts (for example here and here) and it has been in fact a rather long process, so much so that I have made a point of joking in my talks about BCEA that we'd be publishing the book in 2036 $-$ so we're in fact nearly 20 years early...

The official book website is actually already online and I'll prepare another one (I know, I know $-$ it's empty for now!), where we'll put all the relevant material (examples, code, etc).

I think this may be very helpful for practitioners and our ambition is to make the use of BCEA as wide as possible among health economists $-$ even those who do not currently use R. The final chapter of the book also presents our BCEAweb application, which can do (almost everything!) that the actual package can (the nice thing about it is that the computational engine is stored on a remote server and so the user does not even need to have R installed on their machine).

We'll probably have to make this part of the next edition of our summer school...

## Thursday, 26 January 2017

### Three rooms left...

Last December, Kobi and his classmates did their Christmas play, which was based on a relatively close representation of the Nativity (well $-$ perhaps back then shepherds used to run around with most of their hands up their nose, waiving at their parents too...).

Anyway, one of the top acts of the whole thing was something like this, hence the title of the post.

But, more importantly, we're almost running out of single rooms for our summer school, later this year, in Florence (although there are more double rooms)! So book your space soon!

## Monday, 23 January 2017

### Face value

This is actually a not-so-recent paper, but I've only discovered now and I think it's very interesting. The underlying issue is about trying to do "causal inference" from observational data $-$ perhaps one could see this in a simpler way by considering the idea of "balancing" observational data, to mimic as far as possible an experimental setting (and so be able to estimate "causal" effects). [There's lot more on the philosophical aspects behind this problem, which I'm conveniently swiping under the carpet, here...]

Anyway, one of the most popular ways of dealing with this issue of unbalanced background covariates (or generally, confounding) is to use propensity score matching. But, while I think that the idea is somewhat neat and clearly important, what has always bothered me (among other things) is the fact that the resulting outcome model does assume that the estimate of the propensity score (PS) is "perfect" $-$ known with absolute precision, although the basic assumption is that "the PS model needs to be correct". But of course, there's no way of knowing perfectly that the PS model is correct...

So the idea of joining model selection and propagation of uncertainty through the outcome model is actually very interesting. I've only flipped through the paper and I did have some very preliminary ideas in mind on this, so I really want to have a proper look at this!

## Friday, 13 January 2017

### New year resolution

Now that the Christmas break is just a distant memory (Marta would say that I am quite happy with that $-$ she thinks I'm like the Grinch around the Christmas holiday. And she is right), I've given way to my new year's resolution of finally, properly packaging our two R packages that aren't on CRAN yet.

The first one is SWSamp (about which I've already talked here and here) and the second is survHE (which I have also already mentioned here and here).

I've got better at using GitHub and (for survHE) benefited from the help of Peter Konings, who's helped with bits of code and also given me either tips or "forced" me to look into better solutions for the management and potential distribution of the packages, even if they're not directly on the official R repository.

Eventually, this means I've settled for (I think!) a good compromise $-$ I've created a local repository in which I've stored my packages; this in itself doesn't take care of all the dependencies, but it's easy (even for practitioners not too familiar with R) to install the packages and all the others on which they rely to work with very simple commands, for example
install.packages("survHE",
repos=c("http://www.statistica.it/gianluca/R",
"https://cran.rstudio.org",
"https://www.math.ntnu.no/inla/R/stable"),
dependencies=TRUE
)
$-$ this way R uses three repositories (one for survHE, one for all the other dependencies stored on CRAN and one for INLA, which is under its own repository).

We've done some tests and all seems to be working OK, which is great. I've also set in motion a couple of plans for updates to both the packages $-$ I'll post more on this soon! (Incidentally, this also gives way for the development of two more interesting projects: Anthony's work on single arm trial and Andrea's work on missing data for cost-effectiveness analysis. Again, will post more as we have some more output to show for!).

## Friday, 16 December 2016

### Movie stars

Our search for potential alternatives to an academic career, in the face of increasing competition and difficulties in securing grant money has now led Jolene, Marcos and me to seek employment in the show-biz $-$ just in case we fail to recruit enough students for our new MSc in Health Economics & Decision Science...

## Thursday, 15 December 2016

### PhD opportunity!

Applications are invited for a PhD funding opportunity to conduct research in a branch of probability or statistics based in the UCL Department of Statistical Science, commencing in September 2017. This funding is provided by the Engineering and Physical Sciences Research Council (EPSRC).

The requirement for admission to the MPhil/PhD in Statistical Science is a 1st class or high upper 2nd class Bachelor’s degree, or a Master’s degree with merit or distinction, in Mathematics, Statistics, Computer Science, or a related quantitative discipline. Overseas qualifications of an equivalent standard are also acceptable. Further details can be found on the Departmental website. Applicants are expected to prepare an outline proposal of their work. We have some interesting project in our pipeline, including extensions of our work on survHE, or related to evidence synthesis and network meta-analysis, as well as the use of observational data for health economic evaluation.

The studentship will be four years in duration and covers tuition fees at the UK/EU rate plus a stipend of £16,785 per annum for eligible UK residents. EU nationals who have not been ordinarily resident in the UK for 3 years prior to the start of the studentship may still qualify for a fees only award. The studentship may only be awarded to applicants liable to pay tuition fees at the UK/EU rate (i.e. it cannot be used to part-cover overseas tuition fees).

Further information, including details of how to apply, is available here.

### Bayes 2017

We've just opened the call for abstract for the next edition of the Bayes Workshop $-$ this time we're going to Spain and to be more precise to Albacete.

The format is the same as in the past few years $-$ you can send your abstract (including title, authors and not exceeding 300 words) at info@bayes-pharma.org. We're pretty much open to many research areas, as long as they involve Bayesian statistics (I feel I have to say this $-$ in the past we had invariably at least a couple of abstracts that had absolutely nothing to do with a Bayesian analysis!...).

## Friday, 9 December 2016

### Nomen omen

After resisting this for way too long, I've finally decided it was time to release more widely a couple of the R packages I've been working on $-$ I've put them on GitHub, hence the mug...

In both cases, while I think the packages do work nicely, I am still not sure they are ready for an official release on CRAN $-$ effectively, this is mainly due to the fact that documentation may not be super yet, or, more importantly, that I'm still updating some of the basic functions a bit too often.

I knew GitHub was the way to go, but like a grumpy old man I've so far resisted the idea of learning how to manage it. However, because people I wanted to test survHE were struggling to install it (because of its complicated system of dependencies $-$ I'll say a bit more later), I thought this will be a very good alternative.

So, I've created Git repositories for survHE and SWSamp (I've talked about this here) and the packages can be installed by using devtools in R $-$ I think something like this:

install.package("devtools")
install_github("giabaio/survHE")
install_github("giabaio/SWSamp")

I think devtools may fail to install all the dependencies' dependencies under Windows (as far as I understand this is a bug that will be fixed soon) $-$ so the workaround is to use the development version of devtools. Or indulge R and install the missing packages that it requires.