Flexible and efficient Bayesian pharmacometrics modeling using Stan and Torsten, Part I

Abstract

Stan is an open-source probabilistic programing language, primarily designed to do Bayesian data analysis. Its main inference algorithm is an adaptive Hamiltonian Monte Carlo sampler, supported by state-of-the-art gradient computation. Stan's strengths include efficient computation, an expressive language that offers a great deal of flexibility, and numerous diagnostics that allow modelers to check whether the inference is reliable. Torsten extends Stan with a suite of functions that facilitate the specification of pharmacokinetic and pharmacodynamic models and makes it straightforward to specify a clinical event schedule. Part I of this tutorial demonstrates how to build, fit, and criticize standard pharmacokinetic and pharmacodynamic models using Stan and Torsten.