Title: Introduction to PmxStan, An R Library to Facilitate PKPD Modeling with Stan
Date: 6 May 2016, 12:00:00 PM EST
Presenter(s): Yuan Xiong
Skill Level: (Intermediate)
ACOP6 Poster available by clicking below
Continuing the discussion from Introduction to PmxStan: An R Library to Facilitate PKPD Modeling with Stan:
Background: Using a Bayesian approach for making statistical inferences has been gaining popularity in recent years. Stan (http://mc-stan.org) is a Bayesian probabilistic programming language that implements an efficient Hamiltonian Monte Carlo method suitable for fitting larger and more complex models, and these capabilities are attracting more and more users, pharmacometricians in particular.
Currently, two hurdles have largely limited a broad application of Stan in pharmacometrics: 1) a steep learning curve for pharmacometricians to write PKPD model-specific C+±like Stan code; 2) no efficient solvers to work seamlessly with Stan’s No-U-Turn Sampler (NUTS) for ordinary differential equations (ODEs) that are able to handle stiff ODE systems, often encountered in PKPD modeling.
Here we provide an R package called PMXStan to facilitate practical Bayesian PKPD modeling and simulation using Stan.
About the ISOP Pharmacometrics Study Group
- Do you have lab-meetings and learning tutorials in-house in your academic group, company or your neighborhood?
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Where to Sign up or Suggest an Event Topic: https://isop-phmx.github.io/studyGroup/