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B0936
Title: Computation of the Fisher information matrix for discrete nonlinear mixed effect models Authors:  Sebastian Ueckert - INSERM and Universite Paris Diderot (France) [presenting]
France Mentre - University Paris Diderot (France)
Abstract: Despite an increasing use of optimal design methodology for non-linear mixed effect models (NLMEMs) during the clinical drug development process, examples involving discrete data NLMEMs remain scarce. One reason is the limitation of existing approaches to calculate the Fisher information matrix (FIM) which are either model dependent and based on linearization or computationally very expensive. The main challenges in the computation of the FIM for discrete NLMEMs evolve around the calculation of two integrals. First, the integral required to calculate the expectation over the data, and second, the integral of the likelihood over the distribution of the random effects. Monte Carlo (MC), Latin-Hypercube (LH) and Quasi-Random (QR) sampling for the calculation of the first as well as adaptive Gaussian quadrature (AGQ) and QR sampling for the calculation of the second integral are proposed. The resulting methods are compared for a number of discrete data models and evaluated in the context of model based adaptive optimal design.