ACoP6 workshop on Bayesian pharmacometric modeling using Stan

There are still open seats for our post-meeting workshop on Bayesian pharmacometric modeling using Stan. Go to the ACoP6 site ( http://www.acop6.org/acop6-registration) to register. Here’s a more detailed description of the workshop... Getting Started with Bayesian PKPD Modeling Using Stan: Practical use of Stan and R for PKPD applications Instructors: Bill Gillespie and additional Metrum Research Group scientists Where & when: Hyatt Regency Crystal City, Washington Room; 8am - 5pm 8 October 2015 After a brief review of Bayesian principles and computation, we will provide a guided hands-on experience in the use of Stan ( http://mc-stan.org/) for Bayesian PKPD modeling. Stan is a flexible open-source software tool for Bayesian data analysis using Hamiltonian Monte Carlo (HMC) simulation---a type of MCMC simulation. You will execute Bayesian data analysis examples using rstan, a R package interfacing R and Stan. Via the examples you will learn to implement nonlinear regression models, nonlinear mixed effects models and additional programming required for population PK models. The latter includes learning how to deal with dosing and observation event schedules, and with censored data, e.g., BQL data. We will discuss the pros and cons of Stan relative to other tools. Full Price: $600 ISoP Members: $600 ISoP Member Students: $200 Academia/government attendees: $200 Materials provided by Metrum: Course slides, data and code for all examples, online access for one month to a cloud-based compute server on which the software used in the course is installed Requirements for Attendees: Knowledge and experience in PKPD modeling, nonlinear mixed effects modeling and the use of R (or S-PLUS). Basic understanding of Bayesian principles. Participants should bring a laptop that meets the following requirements: - OS: Windows 7 or above, or OS/X 10.7 or above - Browser: Chrome, Firefox, Safari or Internet Explorer 10+ - Internet access via Wi-Fi. Workshop outline - Introduction to Bayesian statistical principles and methods - Bayes Rule - Bayesian modeling & inference process - Computation for Bayesian modeling - Key challenge of Bayesian modeling and inference: sampling from high-dimensional probability distributions - General computational approach: posterior simulation - Brief intro to Markov chain Monte Carlo simulation - Stan basics - What is it? - How do I get it? - How do I run it? - Using rstan - Stan demo: Linear regression - Introduce PK/PD modeling case study to be used throughout the course - Hands-on example 1: Simple nonlinear regression - Topics in Bayesian model development using Stan I - R tools for running Stan and analyzing MCMC simulations - Assessing convergence - Programming hierarchical models (aka mixed effect or population models) - Hands-on example 2: Nonlinear mixed effects - Topics in Bayesian model development using Stan II - User-defined functions - Programming for pharmacometricians: - PK models - Dosing and observation event schedules - Why Stan? - vs ML tools (NONMEM, Monolix, Phoenix, nlme/r, etc.) - vs BUGS variants (WinBUGS, OpenBUGS, JAGS) - Parallel computation with rstan - Hands-on example 3: Population PK. - Additional topics & closing discussion - Dealing with censored data in Stan, e.g., BQL data - Optimizing Stan code - What can you do in Stan that you can’t do with your current tools? - What didn’t we cover?