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
babelmixr2, nlmixr2 and Monolix
As with NONMEM, it is important to be able to compare nlmixr2 to industry standard software like Monolix. With that , in mind, I am proud to announce the first nlmixr2 to Monolix translator in babelmixr2. As with NONMEM, while this has been done before, the method whereby we are converting between the two is novel and has some surprising advantages. How to use Monolix with nlmixr2 To use Monolix in nlmixr, you do not need to change your data or your nlmixr2 dataset.
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