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Nonlinear mixed effects models for repeated measures data.

M L Lindstrom1, D M Bates

  • 1Biostatistics Center, University of Wisconsin-Madison 53706.

Biometrics
|September 1, 1990
PubMed
Summary
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We introduce a new nonlinear mixed effects model for repeated measures data. This model combines least squares and maximum likelihood estimation methods for robust parameter estimation in complex datasets.

Area of Science:

  • Statistics
  • Biostatistics
  • Econometrics

Background:

  • Repeated measures data analysis requires sophisticated statistical models.
  • Existing models for nonlinear and linear mixed effects have limitations.
  • A unified approach is needed for complex data structures.

Purpose of the Study:

  • To propose a general nonlinear mixed effects model for repeated measures data.
  • To define novel estimators for the model parameters.
  • To demonstrate the model's applicability and connections to existing methods.

Main Methods:

  • Development of a nonlinear mixed effects model.
  • Combination of least squares and maximum likelihood estimation techniques.
  • Implementation using Newton-Raphson estimation and established computational methods.

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Main Results:

  • A general nonlinear mixed effects model framework is established.
  • New estimators are defined, integrating strengths of existing methods.
  • The model's practical utility is illustrated through examples.

Conclusions:

  • The proposed model offers a flexible and powerful approach for analyzing repeated measures data.
  • The developed estimators provide a robust method for parameter estimation.
  • This work bridges nonlinear fixed effects and linear mixed effects modeling.