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

Autor: Charles C. Margossian, Yi Zhang, William R. Gillespie
Jazyk: angličtina
Rok vydání: 2022
Předmět:
Zdroj: CPT: Pharmacometrics & Systems Pharmacology, Vol 11, Iss 9, Pp 1151-1169 (2022)
Druh dokumentu: article
ISSN: 2163-8306
DOI: 10.1002/psp4.12812
Popis: 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.
Databáze: Directory of Open Access Journals
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