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Derivation of various NONMEM estimation methods.

Yaning Wang1

  • 1Food and Drug Administration, Office of Clinical Pharmacology, CDER, WO21 RM3662 HFD-880, 10903 New Hampshire Avenue, Silver Spring, MD 20993, USA. yaning.wang@fda.hhs.gov

Journal of Pharmacokinetics and Pharmacodynamics
|July 11, 2007
PubMed
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This paper clarifies objective functions for nonlinear mixed-effects modeling in NONMEM. It details derivations for common estimation methods like FOCE, aiding scientists in data analysis.

Area of Science:

  • Pharmacometrics
  • Statistical Modeling
  • Computational Biology

Background:

  • NONMEM is widely used for nonlinear mixed-effects modeling.
  • Scientists face confusion regarding NONMEM's objective functions and estimation methods.
  • A systematic derivation of core objective functions is lacking.

Purpose of the Study:

  • To provide a detailed derivation of objective functions for common NONMEM estimation methods.
  • To clarify the relationship between different approximation techniques.
  • To compare NONMEM methods with those in SAS and Splus.

Main Methods:

  • Derivation of objective functions for Laplacian, FOCE (with/without interaction), and FO methods.
  • Demonstration using models with homogenous and heterogeneous residual errors.

Related Experiment Videos

  • Analysis of Laplacian and linearized model approximations.

    • Main Results:

    • Systematic derivations for key NONMEM estimation methods are presented.
    • The relationship between objective functions from different approximations is elucidated.
    • Comparisons with SAS and Splus estimation methods are discussed.

    Conclusions:

    • This work reduces confusion surrounding NONMEM objective functions.
    • Understanding these derivations enhances the application of nonlinear mixed-effects models.
    • Provides a valuable resource for researchers using NONMEM, SAS, and Splus.