Bill Gillespie, of Metrum, is giving a tutorial next week at ACoP:
- Getting Started with Bayesian PK/PD Modeling Using Stan: Practical use of Stan and R for PK/PD applications
Thursday 8 October 2015, 8 AM — 5 PM, Crystal City, VA
This is super cool for us, because Bill’s not one of our core developers and has created this tutorial without the core development team’s help. Having said that, we’ve learned a lot from Bill and colleagues on our mailing lists as we were designing ODE solvers for Stan (an ongoing issue—see below for future plans).
Bill’s tutorial is up against a 2-day Monolix tutorial and a 2-day tutorial on R by Devin Pastoor, who’s also been active on our mailing lists recently.
Why Stan for PK/PD?
In case you’re wondering why people would use Stan for this instead of something more specialized like Monolix or NONMEM, it’s because of the modeling flexiblity provided by the Stan language and the effectiveness of NUTS for MCMC. So far, though, we’re in the hole in not having a stiff ODE solver in place. Or a good NONMEM-like event data language on top.
Maybe Bill will jump in with some other motivations.
What’s in Store for Stan’s ODE Solvers?
There’s been lots of behind-the-scenes activity on our ODE solvers—we’re really just getting burned in warmed up.
The next minor release of Stan (2.9) should stop the freezing issue when parameters wander into regions of parameter space that lead to stiff ODEs. And we’ve really sped up the Jacobian calculations when Michael Betancourt realized we were doing a lot of redundant calculation and he and I put a patch in to fix it. We should also allow user-defined control of absolute and relative tolerances.
Next, hopefully by Stan 2.10, we’ll have a stiff solver and maybe a way for users to supply analytic coupled-system gradients and Jacobians. Stay tuned. These new designs are largely being guided by Sebastian Weber and Wenping Wang at Novartis. And of course, by Michael Betancourt working out all the math and Daniel, Michael, and I working out the code with Sebastian’s and Wenping’s input.
We also need to evaluate how well variational inference works for ODE problems. Our early trials are very promising. Then we could replace the max marginal likelihood approach of NONMEM with a very speedy variational inference mechanism allowing much more general models.
There’s more in the works, but the above are the top of our to-do list.