ArticleCPT: pharmacometrics & systems pharmacology2025
Within-chain parallelization-Giving Stan Jet Fuel for population modeling in pharmacometrics.
Article in CPT: pharmacometrics & systems pharmacology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- 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
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Abstract
Stan is a powerful probabilistic programming language designed mainly for Bayesian data analysis. Torsten is a collection of Stan functions that handles the events (e.g., dosing events) and solves the ODE systems that are frequently present in pharmacometric models. To perform a Bayesian data analysis, most models in pharmacometrics require Markov Chain Monte Carlo (MCMC) methods to sample from the posterior distribution. However, MCMC is computationally expensive and can be time-consuming, enough so that people will often forgo Bayesian methods for a more traditional approach. This paper shows how to speed up the sampling process in Stan by within-chain parallelization through both multi-threading using Stan's reduce_sum() function and multi-processing using Torsten's group ODE solver. Both methods show substantial reductions in the time necessary to sufficiently sample from the posterior distribution compared with a basic approach with no within-chain parallelization.
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