The nature of the full-order model
The nature of the model to be reduced is an important factor in choosing the best method for model reduction. Linear models (e.g. most pharmacokinetic models including physiologically based PK models) are the easiest and work well with any method. In contrast, nonlinear models, most pharmacodynamic QSP models, tend to be harder to simplify and many methods need to be adapted to work with them. If the QSP model is based on continuous functions (e.g the Bone model [52]) then most parametric methods would be useful. However models that are a discrete combination of large continuous functions (e.g. the coagulation model [14]) are really only amenable to nonparametric methods. Nonparametric methods are well suited to any type of full-order model and appear to have no requirements about their structure.