Advanced Use of Stan, RStan and Torsten for Pharmacometric Applications

These videos capture most of a one day workshop presented at the PAGE 2018 meeting in Montreux, Switzerland on 29 May 2018.

The workshop provides a guided hands-on experience in the advanced use of Stan, rstan and Torsten 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. Torsten is a Stan extension containing a library of functions to simplify implementation of PKPD models. This workshop builds on the foundations presented in our previous introductory Stan workshops. Topics include model evaluation and comparison, models with systems of ODEs, optimizing Stan code, using MCMC results for population and trial simulations, and more. You will execute Bayesian data analysis examples using Stan. Via the examples, you will learn to implement population PKPD models including those involving censoring, numerical solution of ODEs, and user-defined probability distributions and likelihoods.

Download the zip file containing the course materials. Unzip the file. Navigate to the “script” directory. Open the R script file “pkgSetup.R” using your favorite R environment and run the script to install the R packages used in the course.

MI260: Bayesian Model-Based Meta-Analysis to Support Decision Making in Drug Development

MI260 provides an introduction to meta analysis concepts and methods, with a strong focus on model-based meta-analysis of summary data or a combination of summary and individual data from clinical trials to support decision-making in clinical drug development. Upon completion of the course, participants will be able to write a meta-analysis plan, design a model-based meta-analysis of clinical trial data to address strategic decisions in a clinical drug development program, and implement it using a Bayesian approach executed with WinBUGS and R. Participants will also be able to construct a model for the relationship between an efficacy- or safety-related clinical outcome and independent variables such as dose, time and patient characteristics by analysis of summary data from multiple studies, e.g., treatment means and standard deviations, and construct such a model by analysis of a combination of summary and individual data, as well as execute and interpret population simulations to support decision-making in clinical drug development.

MI250: Introduction to Bayesian PK-PD Modeling & Simulation

MI250 provides an introduction to Bayesian modeling theory and the practical use of WinBUGS and R for PK-PD applications. In addition to basic concepts, this course includes instruction on BUGSModelLibrary, an open-source tool developed by Metrum Institute. This library facilitates the implementation of population PK-PD models in WinBUGS for compartmental models described by algebraic or differential equations.