Highlighting some great work presented at ACOP6 in October.
Poster available by clicking above
Code is available at
Cran Package is also now available at
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.