Nonlinear models for repeated measurement data: An overview and update

Editor’s Invited Article

DOI: 10.1198/1085711032697

Cite this article as:
Davidian, M. & Giltinan, D.M. JABES (2003) 8: 387. doi:10.1198/1085711032697

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.

Key Words

HierarchicalmodelInter-individual variationIntra-individual variationNonlinear mixed effects modelRandom effectsSerial correlationSubject-specific

Copyright information

© International Biometric Society 2003

Authors and Affiliations

  1. 1.Department of StatisticsNorth Carolina State UniversityRaleigh
  2. 2.Genentech, Inc.South San Francisco