Welcome
Why nlmixr?
The goal of nlmixr, or more accurately nlmixr2, is to support easy and robust nonlinear mixed effects models (NLMEMs) in R.
NLMEMs are used to help identify and explain the relationships between drug exposure, safety, and efficacy and the differences among population subgroups. Most often, they are built using longitudinal PK and pharmacodynamic (PD) data collected during clinical studies. These models characterize the relationships between dose, exposure and biomarker and/or clinical endpoint response over time, variability between individuals and groups, residual variability, and uncertainty.
NLMEM development in the pharmaceutical space is dominated by a small number of proprietary, commercial software tools. Although this kind of approach to software has some advantages, adopting an open-source, open-science paradigm also has benefits - third-party auditing or adjustments are possible, and the precise model-fitting methodology employed can be determined by anyone with the time and energy to review the source code. We see nlmixr2 being especially useful in being able to integrate into the rich R ecosystem, and it is well suited for use in scripted, literate-programming workflows of the kind flourishing in the R ecosystem by means of packages such as knitr and rmarkdown.
The nlmixr2 blog
Model diagrams and equations, straight from the code
Watch on YouTube Many pharmacometric reports have the same two items near the top of the methods section: a compartment diagram of the final model and the equations that define it. They are also two of the easiest things to get wrong. The diagram is usually drawn by hand in PowerPoint or Visio early in the analysis. The equations are typed into Word or LaTeX from the control stream. Then the model changes – a transit compartment is added, the effect goes from stimulation to inhibition, a covariate moves – and the code is updated but the figure and the math are not.
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