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Activity Number: 423
Type: Contributed
Date/Time: Wednesday, August 1, 2007 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistical Computing
Abstract - #309841
Title: Generalized Estimating Equations for Nonlinear Mixed Effect Model
Author(s): Lawrence Lee*+ and Jiming Jiang
Companies: University of California, Davis and University of California, Davis
Address: Department of Statistics, Davis, CA, 95616,
Keywords: Gaussian quadrature ; Laplacian approximation ; Linear mixed-effects approximation ; generalized estimation equation
Abstract:

Nonlinear mixed effect models (NMEM) are widely used in pharmacokinetics. Currently, the most popular approaches in inference about NMEM are: Linear mixed-effects approximation, Laplacian approximation and Gaussian quadrature. While these approaches are quite effective computationally, they are known to produce inconsistent estimators of the parameters. We propose a generalized estimation equation approach to the parameter estimation in nonlinear mixed effect model and study its performance by Monte Carlo simulations.

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Revised September, 2007