Job advert

This is an interesting post just advertised at Imperial College London by Marta.

Department of Epidemiology and Biostatistics

School of Public Health

Research Associate in Biostatistics

Salary: £33,410 to £42,380 per annum

Duration: 3 years fixed term

This is an exciting opportunity for a researcher with a PhD in statistics, biostatistics or a related quantitative subject to join the research team at the national MRC-PHE Centre for Environment and Health (http://www.environment-health.ac.uk/). The post is based within the Department of Epidemiology and Biostatistics and will be line managed by Dr Marta Blangiardo.

The post holder will work on the MRC methodology funded grant: “A general framework to adjust for missing confounders in observational studies” to develop a Bayesian statistical approach to integrate different sources of data and to use the propensity score for missing data imputation in the context of epidemiological observational studies. This is a collaborative project between Imperial College, LSHTM and Cambridge MRC-BSU.

You should have a thorough working knowledge of Bayesian modelling and some experience of working with spatial statistics and of analysing epidemiological/biomedical data. Familiarity with R/BUGS is essential. You should be motivated and extremely organised with experience of working in multi-disciplinary teams.

This full-time post is based at the St Mary’s Campus, Paddington and will be fixed term for three years. For informal enquiries please contact Dr Marta Blangiardo: m.blangiardo@imperial.ac.uk. Our preferred method of application is online via our website at http://www3.imperial.ac.uk/employment (please select “Job Search” then enter the job title or vacancy reference number into “Keywords”). Please complete and upload an application form as directed quoting reference number SM137-15AL.

Alternatively, if you are unable to apply online, please email smrecr@imperial.ac.uk to request an application form. Closing date: 22nd July 2015 (midnight BST)

Back log

Last week I went to Madrid to examine a PhD (I've mentioned this in another post). The thesis was focussed on a mixture of computer science and health economics $-$ in particular, much of the work was about developing suitable algorithms for running efficiently Markov models using extensions of tools such as Influence Diagrams. 

I have done some work on related issues when I was doing my own PhD, back in the 1900's, so I was interested in this work and it was good to being "forced" to read about it, now.

The main innovation is the development of algorithms and a specific software, called OpenMarkov. From my perspective, this can be a very good tool, especially when compared with Excel, which is still used by many practitioners to develop their Markov models for economic evaluation.

The main advantage over Excel is that OpenMarkov allows the user to specify a graphical structure for the model and then define a set of conditional probability tables to determine the transition probabilities from one state to another. Interestingly, it is also possible to use (some) probability distributions to represent these, which is good to then perform probabilistic sensitivity analysis (PSA).
What is currently missing is the possibility of propagating evidence to estimate the value of the main parameters (ie the transition probabilities, or functions thereof). So, you have to "know" what the values or distributions are for the transition probabilities when running the model. In this sense, I see OpenMarkov (at least in its current version!) as an "advanced version" of Excel-based models.

This of course has implications in terms of PSA, since while you can allow for multiple parameters to be modelled using a probability distribution, you're likely to miss on the potential underlying correlation. A full Bayesian model (for example like those described in BMHE) would overcome this problem $-$ perhaps at the expense of increasing the model complexity. But, as I said in my talk, if you have complex problems and you want to model them efficiently, then you probably shouldn't be too surprised that the models are complex and suitable tools are needed...

All in all, I did like the idea of OpenMarkov quite a lot, though $-$ particularly, I thought that there may be quite some scope for integrating it with R/BCEA (or similar tools), which would be very useful in many applied cases (as Markov models are extremely popular in health economics!). I may even try and play around with it, if I find a good student willing to do so...

Survival of the fittest (health economic model)

To make up for the fact that we've missed a couple of slots over the past months in our seminar series, we thought we organised a more structured event. 

So, our next seminar will in fact be a workshop and will be held at UCL on 7 July from 1.30pm to 4.30pm, room 102 on the first floor of 1-19 Torrington Place. 

The title is "Statistical issues in modelling survival data for health economic evaluation" and we have an exciting line-up of speakers. 

Nick Latimer will first introduce the general issue of statistical modelling of survival data and extrapolation in health economics and then discuss the interesting issue of treatment switching. Chris Jackson will present and discuss flexible methods to perform extrapolation of survival data and the combination of evidence. Finally, Patricia Guyot will then discuss methods to reconstruct individual survival data from published survival curves.

The tentative timetable of the event is the following:

• 1.30-1.50: Nick Latimer (HEDS, ScHARR. University of Sheffield): Standard survival analysis techniques - methods typically used in HTA
• 1.50-2.35: Chris Jackson (MRC Biostatistics Unit, Cambridge): Improving long-term survival estimation through flexible models, combining evidence and accessible software
• 2.35-2.50: Coffee break
• 2.50-3.15: Nick Latimer (HEDS, ScHARR. University of Sheffield): Methods for adjusting survival estimates in the presence of treatment switching
• 3.15-4.00: Patricia Guyot (Mapi, Utrecht, Netherlands): Reconstructing Kaplan Meier data from published survival curves
• 4.00-4.30: Discussion

If you didn't know about this from our mailing list and still would like to attend, please drop me an email, so we can arrange and avoid space issues!

Video seminar

Later this week, I'm off to Madrid to examine a PhD candidate at UNED (that's the Spanish Open University). This is interesting work on probabilistic network for health economic evaluation, so it should be good.

As part of my trip, I'll also give a seminar in the Master in Advanced Artificial Intelligence programme $-$ I'll sort-of use the talk I gave at the RSS workshop on Bayesian methods in health economics, although I have modified and included some thoughts from my talk at the webinar.

The coolest thing is that they will broadcast the talk on Thursday at 5pm (Spain time): here's the link. I'll then link to the file from our research group webpage $-$ I think this kind of things can be quite useful, although it does mean I'll have to be on my best behaviour while giving the talk...

My talk @ the London Machine Learning Meetup

This Wednesday I've been invited to give a talk at the London Machine Learning Meetup $-$ I don't have a lot of experience of these meetings but I'm told that the audience is typically industry practitioners and some academics, ranging from novices to experienced Machine Learning experts. 

I will give my introduction to INLA (although I've made a few changes to the slides I presented in Rotterdam and then in Girona a while back). Apparently, 150 have said that they are going with 90 more on a waiting list of some sort, so I better rehearse the talk!