Considerable headway has been made over the last 20 or 30 years into the isolation of points of failure in human energy metabolism using metabolic models of challenge data. These models are almost always differential in form, second-order (or higher), nonlinear, and involve both estimated and observed metabolite concentrations. As such, they are usually relatively foreign to the scope of statistical modeling software packages. In this presentation, we demonstrate novel methods for solving and fitting these models to challenge data using Stata, and we illustrate techniques for deriving useful clinical indices such as insulin resistance.
