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

TL;DR

This paper introduces a flexible and efficient Bayesian modeling framework for pharmacometrics using Stan and Torsten, enabling detailed pharmacokinetic and pharmacodynamic analysis with robust diagnostics.

Contribution

It presents an extension of Stan with Torsten for easier specification of pharmacometric models, demonstrating practical application in model building, fitting, and critique.

Findings

Effective Bayesian pharmacometric modeling demonstrated

Enhanced model specification with Torsten functions

Reliable inference with diagnostics

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 which 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.