ArticleCPT: pharmacometrics & systems pharmacology2022
Flexible and efficient Bayesian pharmacometrics modeling using Stan and Torsten, Part I.
Article in CPT: pharmacometrics & systems pharmacology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Bayesian Workflow for Minimal PBPK Models: Case Study of Dapagliflozin.CPT: pharmacometrics & systems pharmacology · 2026Article
- The Regularized Horseshoe for Covariate Selection Improves Convenience and Predictive Performance in Population PK/PD Models.CPT: pharmacometrics & systems pharmacology · 2026Article
- Pharmacometric modeling with the zero-order hold.Journal of pharmacokinetics and pharmacodynamics · 2025Article
- Implementing a Bayesian approach using Stan with Torsten: Population pharmacokinetics analysis of somatrogon.CPT: pharmacometrics & systems pharmacology · 2025Article
- Within-chain parallelization-Giving Stan Jet Fuel for population modeling in pharmacometrics.CPT: pharmacometrics & systems pharmacology · 2025Article
- The Role of Pharmacometrics in Advancing the Therapies for Autoimmune Diseases.Pharmaceutics · 2024Review
- A novel Bayesian generative approach for estimating tumor dynamics from published studies.CPT: pharmacometrics & systems pharmacology · 2024Article
- Bayesian Calibration to Address the Challenge of Antimicrobial Resistance: A Review.IEEE access : practical innovations, open solutions · 2024Article
- Identifying waves of COVID-19 mortality using skew normal curves.Journal of applied statistics · 2024Article
- Bayesian PBPK modeling using R/Stan/Torsten and Julia/SciML/Turing.Jl.CPT: pharmacometrics & systems pharmacology · 2023Article
- Bayesian inference for fitting cardiac models to experiments: estimating parameter distributions using Hamiltonian Monte Carlo and approximate Bayesian computation.Medical & biological engineering & computing · 2023Article
- Flexible and efficient Bayesian pharmacometrics modeling using Stan and Torsten, Part I.CPT: pharmacometrics & systems pharmacology · 2022Article
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Authors and funding
3 authors.
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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.
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