DAE

Abstract

We present an extended approximation of the Fisher Information Matrix (FIM) for nonlinear mixed effects models based on a first order conditional (FOCE) approximation of the population likelihood. Unlike previous FOCE based FIM, we use the empirical Bayes estimates to derive the FIM. In several examples, compared to the old FOCE based FIM, the improved FIM predicts parameter uncertainty much closer to simulation based empirical parameter uncertainty. Furthermore, this approach seems more robust against other approximations of the FIM, i.e. (Full/Reduced FIM). Finally, the new FOCE derived FIM is slightly closer to the simulated empirical precision than the FO based FIM.