Tag Archives: PK/PD

Free workshop on Stan for pharmacometrics (Paris, 22 September 2016); preceded by (non-free) three day course on Stan for pharmacometrics

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So much for one post a day…

Workshop: Stan for Pharmacometrics Day

If you are interested in a free day of Stan for pharmacometrics in Paris on 22 September 2016, see the registration page:

Julie Bertrand (statistical pharmacologist from Paris-Diderot and UCL) has finalized the program:

When Who What
09:00–09:30 Registration
9:30-10:00 Bob Carpenter Introduction to the Stan Language and Model Fitting Algorithms
10:00-10:30 Michael Betancourt Using Stan for Bayesian Inference in PK/PD Models
10:30-11:00 Bill Gillepsie Prototype Stan Functions for Bayesian Pharmacometric Modeling
11:00-11:30 coffee break
11:30-12:00 Sebastian Weber Bayesian popPK for Pediatrics – bridging from adults to pediatrics
12:00-12:30 Solene Desmee Using Stan for individual dynamic prediction of the risk of death in nonlinear joint models:
Application to PSA kinetics and survival in metastatic prostate cancer
12:30-13:30 lunch
13:30-14:00 Marc Vandemeulebroecke A longitudinal Item Response Theory model to characterize cognition over time in elderly subjects
14:00-14:30 William Barcella Modeling correlated binary variables: an application to lower urinary tract symptoms
14:30-15:00 Marie-Karelle Riviere Evaluation of the Fisher information matrix without linearization in
nonlinear mixed effects models for discrete and continuous outcomes
15:00-15:30 coffee break
15:30-16:00 Dan Simpson TBD
16:00-16:30 Frederic Bois Bayesian hierarchical modeling in pharmacology and toxicology / about what we need next
16:30-17:00 Everyone Discussion

 

Course: Bayesian Inference with Stan for Pharmacometrics

The three days preceding the workshop (19–21 September 2016), Michael Betancourt, Daniel Lee, and I will be teaching a course on Stan for Pharmacometrics. This, alas, is not free, but if you’re interested, registration details are here:

It’s going to be very hands-on and by the end you should be fitting hierarchical PK/PD models based on compartment differential equations.

P.S. As Andrew keeps pointing out, all proceeds (after overhead) go directly toward Stan development. It turns out to be very difficult to get funding to maintain software that people use, because most funding is directed at “novel” research (not software development, research, which means prototypes, not solid code). These courses help immensely to supplement our grant funding and let us continue to maintain Stan and its interfaces.

Stan Course up North (Anchorage, Alaska) 23–24 Aug 2016

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Stan logo
Daniel Lee’s heading up to Anchorage, Alaska to teach a two-day Stan course at the Alaska chapter of the American Statistical Association (ASA) meeting in Anchorage. Here’s the rundown:

I hear Alaska’s beautiful in the summer—16 hour days in August and high temps of 17 degrees celsius. Plus Stan!

More Upcoming Stan Events

All of the Stan-related events of which we are aware are listed on:

After Alaska, Daniel and Michael Betancourt will be joining me in Paris, France on 19–21 September to teach a three-day course on Pharmacometric Modeling using Stan. PK/PD in Stan is now a whole lot easier after Sebastian Weber integrated CVODES (pun intended) to solve stiff differential equations with control over tolerances and max steps per iteration.

The day after the course in Paris, on 22 September, we (with Julie Bertrand and France Mentre) are hosting a one-day Workshop on Pharmacometric Modeling with Stan.

Your Event Here

Let us know if you hear about other Stan-related events (meetups, courses, workshops) and we can post them on our events page and advertise them right here on the blog.

Stan PK/PD Tutorial at the American Conference on Pharmacometrics, 8 Oct 2015

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Bill Gillespie, of Metrum, is giving a tutorial next week at ACoP:

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.

PK/PD Talk with Stan — Thu 8 Oct, 10:30 AM at Columbia: Improved confidence intervals and p-values by sampling from the normalized likelihood

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Sebastian Ueckert and France Mentré are swinging by to visit the Stan team at Columbia and Sebastian’s presenting the following talk, to which everyone is invited.

Improved confidence intervals and p-values by sampling from the normalized likelihood

Sebastian Ueckert (1,2), Marie-Karelle Riviere (1), France Mentré (1)

(1) IAME, UMR 1137, INSERM and University Paris Diderot, Paris, France; (2) Pharmacometrics Research Group, Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden

10:30 AM, Thursday 8 October
1025 School of Social Work Building (CUSSW); 1255 Amsterdam Ave (122 St & Amsterdam)

Asymptotic theory-based statistics such as confidence intervals (CI) and p-values (PVAL) are the basis for most model-driven decisions in drug development. For small sample sizes these approximations do not hold and resampling methods are employed. Sampling from the normalized likelihood function represents an alternative, which with the development of Hamiltonian Monte-Carlo (HMC) methods becomes computationally attractive. In this presentation the results of a comparison between HMC-based sampling and existing approaches for the calculation of CI and PVAL is presented.

The comparison was performed with a simulation study using a one-compartment model and different study sizes. For CI, evaluation was based on runtime, median CI and coverage, and in comparison to CI obtained via covariance matrix, log-likelihood profiling and non-parametric bootstrap. For PVAL, the evaluation was based on runtime, type-I error and power, and in comparison to PVAL obtained via Wald test, log-likelihood ratio test and permutation test. The HMC-based methods were implemented using S with improper or uniform priors for sampling. All asymptotic theory and resampling-based results were obtained in NONMEM 7.3.

The simulations showed good agreement between approaches for large sample sizes and increasing differences for smaller sample sizes. In contrast to most other methods, HMC showed nominal coverage and type-I error at all study sizes. In terms of computation time the HMC-based methods were between 10 and 60 times faster than resampling methods.

In conclusion, CI and PVAL through sampling from the normalized likelihood using HMC yielded results with good theoretical properties at a drastically shorter runtime than resampling methods.