Just after Easter, I'll go for a very quick trip to lovely Girona, where Marc Saez has invited me to give two talks.

The first one will be a re-run of the short course on INLA that I did at Bayes Pharma last year. It's scheduled (and prepared) as a 3-hours talk, in which I briefly introduce the general Bayesian problem, quickly glance over the basics of computation and then spend some time on INLA, describing the theory behind it and then presenting a few examples on how you use it for inference. I've modified the talk slightly and changed a couple of examples.

That will take up the morning and after an half hour break I'll give another seminar; this time I'll talk about the structural zero model. I have presented this a couple of times before, but I think in all cases it was before I finalised the model. So this time the talk should be better.

# Gianluca Baio's blog

Bayesian statistics, health economics and random stuff

## Friday, 18 April 2014

## Wednesday, 16 April 2014

### The Granville incident

Earlier this morning, there was some commotion on the allstat mailing list (if you don't know what it is, that's a UK-based discussion list specifically focussed on statistics; it's been active for quite some time and usually you get useful information on mostly, but not exclusively academic vacancies, conferences, etc).

Vincent Granville is a self-described "visionary data scientist" who often sends emails to the list $-$ most of which the list does not seem to receive with pleasure...

This has of course unleashed a reaction of anger and indignation among the users of the list (although Michael Bretscher from Imperial College noticed that

I agree with the general sentiment and I think that Granville is coming from a very, very different place than basically everybody else who's active on the list. Some people has suggested banning him altogether, which I can sympathise with. But even more simply, is this not a classic situation in which your spam filter becomes your best friend?...

Vincent Granville is a self-described "visionary data scientist" who often sends emails to the list $-$ most of which the list does not seem to receive with pleasure...

*[***NB**I believe that for today, Granville has sought and received more than his fair share of publicity, so I won't link to his webpage $-$ you can google him if you're interested]*Anyway, in his email today, Granville has openly offered a 250 dollars reward to write a review of his book on the Amazon page. In fact, he said he would reward the four "best" reviews (it is not clear what "best" means in this case $-$ I suspect it's to do with how enthusiastic they are...).*

This has of course unleashed a reaction of anger and indignation among the users of the list (although Michael Bretscher from Imperial College noticed that

*"ironically, if all corrupt activities were communicated so transparently, the world might be a better place...**"*).I agree with the general sentiment and I think that Granville is coming from a very, very different place than basically everybody else who's active on the list. Some people has suggested banning him altogether, which I can sympathise with. But even more simply, is this not a classic situation in which your spam filter becomes your best friend?...

## Monday, 14 April 2014

### Bayes Pharma 2014 - nearly there...

We're nearly done with (most of) the preparation for Bayes Pharma 2014. We've received quite some abstracts for the contributed talks $-$ different topics but in general very interesting work, I thought.

We've managed to secure several sponsorship $-$ we kind of look like one of those weird football shirts where they stick up as many brands as they possibly can. But it was nice to involve ISBA and the RSS (in addition to the Quetelet society, who have a long standing association with the conference).

I've nearly finalised the social event (I'll post some gossip as we get closer) and all I'm left to do is to arrange lunches/coffees et al. There are still some available places $-$ all the information needed for registration is here.

We've managed to secure several sponsorship $-$ we kind of look like one of those weird football shirts where they stick up as many brands as they possibly can. But it was nice to involve ISBA and the RSS (in addition to the Quetelet society, who have a long standing association with the conference).

I've nearly finalised the social event (I'll post some gossip as we get closer) and all I'm left to do is to arrange lunches/coffees et al. There are still some available places $-$ all the information needed for registration is here.

**As always, students go free!**### Causal Inference in Health, Economic and Social Sciences

The programme of the forthcoming UK Causal Inference Meeting "Causal Inference in Health, Economic and Social Sciences" is just out. The short conference will be at the end of the month (28th and 29th of April) at the University of Cambridge.

I indirectly feature as Aidan (who's part of our RDD team) is giving a presentation in one of the sessions. His talk is entitled "The Effect of Prior Beliefs on Causal Effect Estimators within a Bayesian Regression Discontinuity Design" and basically comes out as a follow up to our paper.

The idea is to implement the RDD within a full Bayesian framework and to actually assess carefully what's the gain of doing it this way. As is often the case, in some situations, there may be unwanted impact of the prior on the causal estimators, although generally there are advantages (both computationally and in terms of including extra sources of information $-$ that is crucial, especially when you want to go beyond statistical analysis, in my opinion).

I indirectly feature as Aidan (who's part of our RDD team) is giving a presentation in one of the sessions. His talk is entitled "The Effect of Prior Beliefs on Causal Effect Estimators within a Bayesian Regression Discontinuity Design" and basically comes out as a follow up to our paper.

The idea is to implement the RDD within a full Bayesian framework and to actually assess carefully what's the gain of doing it this way. As is often the case, in some situations, there may be unwanted impact of the prior on the causal estimators, although generally there are advantages (both computationally and in terms of including extra sources of information $-$ that is crucial, especially when you want to go beyond statistical analysis, in my opinion).

## Sunday, 6 April 2014

### Seven (a-day)

This week (among other things, including my Vespa breaking down twice in three days) I was busy taking part in an interview panel for a research associate position, together with colleagues in the Medical School at UCL.

One of the questions we were asking to the candidates was about commenting a new study (incidentally, by researchers at UCL) which using data from the Health Survey for England argued that the current "optimal" regime of consuming 5 portions of vegetables and fruit per day could (should) be in fact increased to at least 7, to reduce risk of death.

The point of the question was of course to get the candidates to recognise the possibility of confounding $-$ of course people consuming more veg & fruit might have a much lower risk of death to start with, due to different life-style, etc. (A few candidates got it right straight away, others less so).

But I think this has even more interestingly (

One of the questions we were asking to the candidates was about commenting a new study (incidentally, by researchers at UCL) which using data from the Health Survey for England argued that the current "optimal" regime of consuming 5 portions of vegetables and fruit per day could (should) be in fact increased to at least 7, to reduce risk of death.

The point of the question was of course to get the candidates to recognise the possibility of confounding $-$ of course people consuming more veg & fruit might have a much lower risk of death to start with, due to different life-style, etc. (A few candidates got it right straight away, others less so).

But I think this has even more interestingly (

*eg*, economic) implications in terms of the actual applicability of the health policy, in its current form as well as in terms of potential modifications, like this article in today's Guardian (which I thought was spot-on!) suggests.## Friday, 21 March 2014

### The sampling frame is a list, but not every list is a sampling frame

Yesterday and today, I spent some time marking the in-course assessment (ICA) for my course (the teaching term is over next week $-$ yay!).

The course is called "Social Statistics" and it's intended to deal with surveys and sampling. However, since I inherited 3 years ago, I've tried to include more material on missing data and some stuff about clustering too, with a view to teaching some modelling.

For the ICA, this year I decided that I would randomly group the students and have them

All in all, I was impressed by the creativity of most groups in selecting the topics for their surveys. Some groups did a very good job getting all the parts right $-$ stuff like realising that you can't really extend your inference to a much larger population if you only have a small convenience sample (which may still be a reasonable choice, given time & resources constraints), or going out of their way to find a proper list so that they can use simple random sampling.

A few groups, however, used the list of emails for all the students enrolled in the class (which could be a sampling frame $-$ if the target population were the class!) as it were a sampling frame for a much larger target population (eg the whole of UCL students).

The course is called "Social Statistics" and it's intended to deal with surveys and sampling. However, since I inherited 3 years ago, I've tried to include more material on missing data and some stuff about clustering too, with a view to teaching some modelling.

For the ICA, this year I decided that I would randomly group the students and have them

*do*a quick survey. Admittedly the time was quite short (only 1 week from assignment) and when they got the ICA we were basically only halfway through the course, so they didn't really know about important stuff (such as more advanced sampling methods, or sample size calculations).All in all, I was impressed by the creativity of most groups in selecting the topics for their surveys. Some groups did a very good job getting all the parts right $-$ stuff like realising that you can't really extend your inference to a much larger population if you only have a small convenience sample (which may still be a reasonable choice, given time & resources constraints), or going out of their way to find a proper list so that they can use simple random sampling.

A few groups, however, used the list of emails for all the students enrolled in the class (which could be a sampling frame $-$ if the target population were the class!) as it were a sampling frame for a much larger target population (eg the whole of UCL students).

## Thursday, 13 March 2014

### Canada et al

1. Today was the first day of our course on Bayesian methods in health economics. After my lecture on intro to health economics, Chris has given 2 lectures on Bayesian methods and their implementation in BUGS and then Richard has talked about Bayesian analysis of individual-level data (on costs and then both costs & benefits). In between the lectures, we did BUGS-based practicals $-$ so all in all it was quite heavy on the participants. But nobody gave clear signs of imminent crisis (in fact, we've had quite a few interesting questions!)...

2. We're being extremely lucky, weather-wise. We're told that last week it was around -25C (and it still shows: the river Saskatchewan is completely frozen $-$ I mean: solid!), but now it's quite nice and pleasant $-$ sunny and around 10/15C. Of course, Canadians are in mid-August mood and we've seen quite a few kids in shorts. I wouldn't quite go as far as that, but we can't really complain!

3. I'm told of a nice review of BMHE, which is just appeared on Biometrics. The author of the review says

2. We're being extremely lucky, weather-wise. We're told that last week it was around -25C (and it still shows: the river Saskatchewan is completely frozen $-$ I mean: solid!), but now it's quite nice and pleasant $-$ sunny and around 10/15C. Of course, Canadians are in mid-August mood and we've seen quite a few kids in shorts. I wouldn't quite go as far as that, but we can't really complain!

3. I'm told of a nice review of BMHE, which is just appeared on Biometrics. The author of the review says

4. Quick update on me getting all emotional for no reason while on an intercontinental flight: nothing to report. I struggled a bit at the end of this episode ofThe book seems to be suitable for researchers and practitioners who want to learn and apply statistical methods to health economics. Also it can be a good text for graduate courses in statistical analysis of health economic data. The author tries to keep mathematics at a low level and provides many interesting figures and tables for the readers with weak mathematical / statistical background. It provides step-by-step guidance to practical application of the Bayesian methods by using popular statistical software R and BUGS/JAGS. This would be very attractive to practitioners for they can easily implement Monte Carlo simulation methods necessary for Bayesian inference without fear.

*How I met your mother*, but nothing major $-$ not even a real tear. In any case, to avoid embarrassment, I stopped watching TV.## Monday, 10 March 2014

### Man at work(-ish)

Perhaps one could argue that the obvious, manly activity to do at the weekend when you're home alone is to put and organise stuff in the garage. Well, I was home alone last weekend and my very own version of this was to arxiv the first paper coming out from our research on the regression discontinuity design (RDD) $-$ I know: probably *not* so manly. I did watch rugby and football, though....

The main of points of the papers are these:

1. How and why the RDD can be effectively applied to primary care data. The RDD works when there is some sort of external guideline that decides the allocation to some intervention $-$ drugs are often regulated so that patients with a certain profile

2. The implications of including genuine prior information in such an analysis. In our case study (prescription of statins), there's typically a lot of evidence coming from RCTs; and this may be the case in other areas where a recommendation exists to regulate prescriptions.

I think the plan is to explore next a few interesting (both methodological and substantial) matters, such as how this can be extended to non-continuous outcomes, or used to identify the "optimal" threshold for prescription, based on available primary care data (in addition to RCTs evidence).

The paper can be downloaded here.

The main of points of the papers are these:

1. How and why the RDD can be effectively applied to primary care data. The RDD works when there is some sort of external guideline that decides the allocation to some intervention $-$ drugs are often regulated so that patients with a certain profile

*should*be given them (although, as we discuss, this is often a lot less clear cut...);2. The implications of including genuine prior information in such an analysis. In our case study (prescription of statins), there's typically a lot of evidence coming from RCTs; and this may be the case in other areas where a recommendation exists to regulate prescriptions.

I think the plan is to explore next a few interesting (both methodological and substantial) matters, such as how this can be extended to non-continuous outcomes, or used to identify the "optimal" threshold for prescription, based on available primary care data (in addition to RCTs evidence).

The paper can be downloaded here.

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