In the spotlight: Nonlinear multilevel mixed-effects models
You have a model that is nonlinear in the parameters. Perhaps it is a model of tree growth and therefore asymptotes to a maximum value. Perhaps it is a model of serum concentrations of a drug that rise rapidly to a peak concentration and then decay exponentially. Easy enough: use nonlinear regression ([R] nl) to fit your model. But ... what if you have repeated measures for each tree or repeated blood serum levels for each patient? You might like to account for the correlation within tree or patient. You might even believe that each tree has its own asymptotic growth. You need nonlinear mixed-effects models—also called nonlinear hierarchical models or nonlinear multilevel models.
Stata 15's new menl command can handle everything described above and much more. In my recent blog post, I walk you through a few examples and demonstrate how to use Stata to fit nonlinear models to multilevel data, including repeated-measures data or panel data.
— Houssein Assaad
Senior Statistician and Software Developer