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Nonlinear models for repeated measurement data: An overview and update | SpringerLink

Nonlinear models for repeated measurement data: An overview and update

Abstract

Nonlinear mixed effects models for data in the form of continuous, repeated measurements on each of a number of individuals, also known as hierarchical nonlinear models, are a popular platform for analysis when interest focuses on individual-specific characteristics. This framework first enjoyed widespread attention within the statistical research community in the late 1980s, and the 1990s saw vigorous development of new methodological and computational techniques for these models, the emergence of general-purpose software, and broad application of the models in numerous substantive fields. This article presentsan overview of the formulation, interpretation, and implementation of nonlinear mixed effects models and surveys recent advances and applications.

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Correspondence to Marie Davidian.

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Davidian, M., Giltinan, D.M. Nonlinear models for repeated measurement data: An overview and update. JABES 8, 387 (2003). https://doi.org/10.1198/1085711032697

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Key Words

  • Hierarchicalmodel
  • Inter-individual variation
  • Intra-individual variation
  • Nonlinear mixed effects model
  • Random effects
  • Serial correlation
  • Subject-specific