Monday, 10 October 2016

Masters of some

This is really exciting $-$ well, at least for us... Our new Masters in Health Economics and Decision Sciences is up and running and applications are now open for the next academic year!

We're updating the promotional material (which will also feature a rather embarrassing interview that I and others have filmed to describe the life-changing advantages people will experience if they come and study with us), so I'll point to more (hilarious, but hopefully also helpful) bits as they become available.

In the meantime, we thought we'd include some ideas of what the syllabus might look like, for different students' backgrounds (in brackets the departments/institutes within UCL already providing the module).

Sample module selection for a student with a first degree in Epidemiology or Statistics (or any other quantitative discipline aside from Economics), wishing to take the Decision Science Stream within the degree
1. Health Systems in a Global Context (Institute of Global Health, IGH)
2. Economic Evaluation (IGH) 
3. Medical Statistics I (Statistical Science, Stats)
4. Key Principles of Health Economics
5. Introductory Microeconomics  
6. Modelling for Decision Science
7. Bayesian Methods in Economic Evaluation
8. Medical Statistics II (Stats)

Sample module selection for a student with a first degree in Economics, wishing to take the Decision Science Stream within the degree
1. Health Policy and Reform  (Centre for Philosophy Justice and Health, CPJH)
2. Economic Evaluation (IGH) 
3. Medical Statistics I (Stats)
4. Microeconomics for Health
5. Modelling for Decision Science
6. Bayesian Methods in Economic Evaluation
7. Health Economics (Economics, Econ)
8. Urban Health (IGH)

Sample module selection for a student with a first degree in Epidemiology or Statistics (or any other quantitative discipline aside from Economics), wishing to take the Economic Stream within the degree
1. Health Systems in a Global Context (IGH)
2. Introductory Microeconomics  
3. Econometrics (NEW)
4. Economic Evaluation (IGH) 
5. Microeconomics for Health
6. Health Economics (Econ)
7. Medical Statistics I (Stats)
8. Bayesian Methods in Economic Evaluation

Sample module selection for a student with a first degree in Economics, wishing to take the Economics Stream within the degree
1. Health Policy and Reform  (CPJH)
2. Econometrics (NEW)
3. Economic Evaluation (IGH) 
4. Microeconomics for Health
5. Health Economics (Econ)
6. Modelling for Decision Science (NEW)
7. Bayesian Methods in Economic Evaluation

8. Social Determinants of Health

We've already received some applications, which is super good news $-$ but there's still plenty of room!

Friday, 7 October 2016

Shiny happy people in the land of the Czar

During the summer, we've worked silently but relentlessly to set up a departmental server that could run R-Shiny applications. 

There's a bunch of us in the department doing work on R and producing packages and so we thought it'd be a good idea to disseminate our research. Which is just as well, as I've been nominated "2020 REF Impact Czar", meaning I'll have to help collate all the evidence that our work does have an impact on the "real world"...

Anyway, after some teething problems (mainly due to my getting familiar with the system and the remote installation of R and Shiny), I think we've now managed to successfully "deploy" (I think that's the correct technical term) two webapps. 

These are bmetaweb and BCEAweb. The first one is the web-interface to our bmeta package for Bayesian meta-analysis (which I developed with my PhD student Christina). The main point of bmeta is to allow some sort of standardised framework for a set of models for meta-analysis, depending on the nature of the outcome and some modelling assumption (eg fixed vs random effects). In addition to running the default models (which are based on rather vague priors and pre-defined model structures), bmeta saves data and model code (in JAGS), so that people can actually use these templates and actually modify them to their specific needs.

BCEAweb is the actual mother of the whole project (much as SAVI is then the actual grandmother, as it inspired our work on developing web-interfaces to R packages) and the idea is to use remotely BCEA to post-process the outcome of a (Bayesian) health economic model. BCEAweb works by uploading the simulations from a model and then using remotely R to produce all the relevant output for the reporting of the results in terms of cost-effectiveness analysis.

One thing we've tried very hard to include in both the webapps is the possibility of downloading a full report (in .pdf or .docx format) with a summary of the analyses. I think this is really cool and we'll probably develop more of these $-$ particularly for our work related to statistical methods for health economic evaluations.

Comments welcome, of course!

Thursday, 15 September 2016

The fix

This is a very interesting post by Martyn Plummer on the JAGS News blog, describing how apparently silly details may make a world of difference. I think Martyn says he's now fixed the issue (basically, it appears that JAGS was sensitive to the order in which the model was written, eg at compilation you may have a staggering difference $-$ 16 minute vs 8 seconds $-$ depending on whether you defined the deterministic relationships between parameters first or last).

I'm writing this post mostly as a signpost for myself $-$ I guess you always encounter issues like this, which seem trivial, and the fix is so easy $-$ if only you had a bunch of little workers at your disposal all the time...

LGM 2016

Yesterday I went to beautiful Bath for The Fifth Workshop on Bayesian Inference for Latent Gaussian Models with Applications and give a talk on our work on INLA-SPDE to compute the Expected Value of Partial Perfect Information.  

I couldn't stay for the whole three days, which is a shame because yesterday was really interesting. In the morning, Mike Betancourt (I'm not sure the page I'm linking here is his "official" one, as he's left UCL now) gave an excellent tutorial on Stan. I really enjoyed the morning playing around with the code $-$ in fact, I think we'll try and use it more and more (for example, I will try and integrate this into survHE).

Then in the afternoon, there were two interesting sessions, on talks that had obviously the common thread of LGMs, but were in fact quite diverse. I liked that too!  

Tuesday, 13 September 2016

Careful whisper

PREFACE: This post is only partially a grumpy man's emotional outburst: just hear me out on this one... 

ABSTRACT: A grumpy man vents about spam emails from random scientific (and sometimes pseudo-scientific) journals.


MAIN TEXT: First off, I should say that, luckily, my spam filter works pretty well, so I normally don't really get to see these messages (except for when I take 5 minutes to check what's ended up in the spam $-$ typically these are my 5 minutes of fun...). 

But, I find it super-hilarious to read the weird invitations to contribute papers to the most bizarre journals (that is bizarre with respect to my own field of expertise, of course $-$ they are often good journals, although I think that sometimes the weirdness goes hand in hand with their ridiculousness...). 

Anyway, I particularly really, really like when they start the email with something like this: 
Dear Dr Gianluca@my email address, [of course, to these people my email address is my full name]
We follow very carefully your research and we are impressed by your scientific production. We would be delighted if you could contribute a paper (possibly within the next 20 minutes) to the Journal of Something that has absolutely nothing to do with Statistics, or Health Economics, or anything you've ever done in your life since you were 4 and accidentally sat on your mum's little cactus and all the spiny stems pricked your bottom. [and that, sadly, is a true story...]
Now, that's what I call carefully following somebody's research!

CONCLUSIONS: Incidentally, one of my biggest regrets in life is to have never managed to wear my hair like George Michael. And that's something I've carefully tried to do when I was 14.

Friday, 26 August 2016

Sad night

I've just heard the very sad news that Richard Nixon has passed away this morning. I can't say I knew Richard very well, but I thought he really was a lovely guy and I am very saddened.

I knew of him (among other things) through his work on covariate adjustment in health economic evaluations, which I think was part of his PhD at the MRC Cambridge. I then got in contact with him more closely when I was thinking of organising the short course based on BMHE, since he and Chris were already doing something like that. I suggested we did the course together and he was very enthusiastic about it. In fact, when he was asked to teach a short course at the University of Alberta, he said the three of us should have a go, which we did. Then we taught the course at Bayes 2014UCL and at a one-day workshop organised by the RSS. He fell ill just before the last edition of the course.

Tonight I have a very vivid memory of the time we were in Edmonton having dinner after the first night of the course when I told that for some reason Italians usually get really crossed about chicken in pizza and that he used to tease me with that every time we've met since, saying that he would love a pizza with chicken. And how we used to introduce ourselves to the audience $-$ and how sometimes people were to young to get the references. I'll miss you, Richard.

Wednesday, 17 August 2016

National lottery

Yesterday, many British newspapers have covered the news of the new Dementia Atlas, released by the Department of Health.

As far as I can see, the atlas uses data from a variety of sources (including the Quality Outcomes Framework, QOF, scheme, which collects information from general practices around the country, providing incentives to the doctors to record data on key indicators).

So far so good $-$ nothing wrong with that. In fact, cool representation with maps highlighting geographical variation across England and providing rates for several summary statistics, eg prevalence of dementia, level of diagnosis, etc. As usual, though, the media couldn't resist jumping on the news and making a meal of it, mostly by presenting it with grand headlines, which in many cases missed the point, or bluntly mis-represented reality, I think.

For example, beloved Daily Mail and The Telegraph yell about "Post-code lottery in care". Now, it may well be that the data reveal massive inequality in the access to care and diagnosis across the country, which is a very good thing to expose in order to tackle it and then remove it or at least limit it $-$ that's in the spirit of the NHS. But, although I think the website should have made a much better job at explaining the numbers reported, it appears that the information presented in the maps is about the raw rates! It's not quite clear then whether the background characteristics of each area (defined in terms of Clinical Commissioning Group, CCG) do play a role in explaining away some of the differences in the actual rates for each of the measures reported in the table. 

So may well be that we're playing Peter Griffin's lottery with people's health. Or there may be much more than that. But some media just don't care about which is which...

Friday, 15 July 2016

Finish line (nearly)

We are very close to the finish line $-$ that's being able to finally submit the BCEA book to the editor (Springer).

This has been a rather long journey, but I think the current version (I dread using the word "final" just yet...) is very good, I think. We've managed to respond to all the reviewers' comments, which to be fair were rather helpful and so this should have improved the book. 

Anna and Andrea have done very good work and I didn't even have to play the bad, control freak guy to have them prepare their bits quickly $-$ in fact, I think at several points, I've been late in doing mine... 

Here's the (somewhat simplified to only sections and sub-sections) table of content:
  1. Bayesian analysis in health economics
    1. Introduction
    2. Bayesian inference and computation
    3. Basics of health economic evaluation
    4. Doing Bayesian analysis and health economic evaluation in R
  2. Case studies
    1. Introduction
    2. Preliminiaries: computer configuration
    3. Vaccine
    4. Smoking cessation
  3. BCEA - a R package for Bayesian Cost-Effectiveness Analysis
    1. Introduction
    2. Economic analysis: the bcea function
    3. Basic health economic evaluation: the summary command
    4. Cost-effectiveness plane
    5. Expected Incremental Benefit
    6. Contour plots
    7. Health economic evaluation for multiple comparators and the efficiency frontier
  4. Probabilistic Sensitivity Analysis using BCEA
    1. Introduction
    2. Probabilistic sensitivity analysis for parameter uncertainty
    3. Value of information analysis
    4. PSA applied to model assumptions and structural uncertainty
  5. BCEAweb: a user-friendly web-app to use BCEA
    1. Introduction
    2. BCEAweb: a user-friendly web-app to use BCEA
Throughout the book we use a couple of examples of full Bayesian modelling and the relevant R code to run the analysis and then use BCEA to do the "final part" of the cost-effectiveness analysis.

We've tried to avoid unnecessary complications in terms of maths, but we do include explanations and formulae when necessary. It was difficult to strike a balance for the audience $-$ especially as it was complicated to define what the audience would be... I think we're aiming for statisticians who want to get to work in health economic evaluations and health economists who need to use more principled statistical methods and software (I couldn't resist in several points moving my tanks to invade Excelland and replace the local government with R officials...).

The final chapter also present and discuss the use of graphical front-ends to R-based models (eg as in SAVI) $-$ we have a BCEA front-end too. I think these may be helpful, but they can't replace making people in the "industry" more familiar with full modelling and away from spreadsheets and stuff (these work when the models are simple. But the models that are required are not very often that simple...).

We also present lots of work on value of information (including our recent work), which is also linked to our short course. May be it's time to link BMHE and this to do a long course... (there's more on this to come!)