user2016

We present a domain-specific mini-language embedded in R for expressing pharmacometrics and drug-disease models. Key concepts in our RxODE implementation include A simple syntax for specifying models in terms of ordinary differential equations (ODE). A compilation manager to translate, compile, and load machine code into R for fast execution. An 'eventTable' closure object to express inputs/perturbations into the underlying dynamic system being modeled. Model reflectance to describe a model's structure, parameters, and derived quantities (useful for meta-programming, e.g., automatic generation of shiny applications). We present examples in the design of complex drug dosing regimens for first-in-human studies via simulations, the modeling of unabated Alzheimer disease progression, and time-permitting, the modeling of visual acuity among age-related macular degeneration patients in the presence of disease-mitigating therapies. We also compare our approach in RxODE to similar work, namely, deSolve, mrgsolve, mlxR, nlmeODE, and PKPDsim. In closing, we use this 40th anniversary of the S language to reflect on the remarkably solid modeling framework laid out almost 25 years ago in "Statistical Models in S" (Chambers and Hastie (1992)), and to identify new challenges for specifying and fitting increasingly more complex statistical models, such as models of dynamic systems (as above), models for multi-state event history analysis, Bayesian data analysis, etc.