RE: Subpopulation

Dear Huali, The best is to derive one model for all data. If you are pressed with time it may be sufficient to describe the data in the dose range which is clinically relevant (if known). Possibly, in this dose range there is no nonlinerarity. However, splitting the subjects based on the outcome is not a good idea. The two models you end up with will both be biased in the parameter estimates since subjects with e.g. high Km (or slow absorption/high Vmax) will be more abundant in the linear-kinetics dataset and vice versa for the nonlinear-kinetics dataset*. Additionally, without a model it is difficult to distinguish an initial nonlinearity from the absorption process, so that borderline cases may end up in the wrong dataset. The nonlinearity may only be relevant for a subpopulation of your study subjects. This can be investigated in a mixture model, in case a single distribution of parameter values can not describe your data. Before making such an attempt, try to understand the possible sources of nonlinearity in your specific case, so that the model captures this. I hope this helps! Jakob *I do not know the source of nonlinearity in the specific case, so this just to exemplify with nonlinear CL.