NONMEM, a program package the produces the extended least squares estimates of population parameters for a nonlinear mixed-effect model, has been applied to two data sets from patients routinely receiving phenytoin. A general model for the data is proposed. The models used in previous, standard-method analyses of each data set are compared to the general model using NONMEM. The comparison involves two questions: The first asks whether the parameters estimated previously agree with NONMEM estimates when the original model is used. We find that for fixed-effect parameters they generally do, while for interindividual random-effect parameters the previous methods’ estimates appear upward biased relative to NONMEM. Second, the original model per se is compared to the general model by comparing the best fit to each. The general model is clearly superior. NONMEM’s ability to distinguish among models, and to precisely estimate their parameters from sparse individual data, is illustrated and verified.
By Lewis B. Sheiner & Thaddeus H. Grasela