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...