Nonlinear mixed-effects regression

In many applications, such as biological and agricultural growth processes and pharmacokinetics, the time course of a continuous response for a subject over time may be characterized by a nonlinear function. Parameters in these subject-specific nonlinear functions often have natural physical interpretations, and observations within the same subject are correlated. Subjects may be nested within higher-level groups, giving rise to nonlinear multilevel models, also known as nonlinear mixed-effects or hierarchical models. The new Stata 15 command menl allows you to fit nonlinear mixed-effects models, in which fixed and random effects may enter the model nonlinearly at different levels of hierarchy. In this talk, I will show you how to fit nonlinear mixed-effects models that contain random intercepts and slopes at different grouping levels with different covariance structures for both the random effects and the within-subject errors. I will also discuss parameter interpretation and highlight postestimation capabilities.