Nonlinear models for repeated measurement data: An overview and update

被引:271
作者
Davidian, M
Giltinan, DM
机构
[1] N Carolina State Univ, Dept Stat, Raleigh, NC 27695 USA
[2] Genentech Inc, San Francisco, CA 94080 USA
关键词
hierarchical model; inter-individual variation; intra-individual variation; nonlinear mixed effects model; random effects; serial correlation; subject-specific;
D O I
10.1198/1085711032697
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
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 presents an overview of the formulation, interpretation, and implementation of nonlinear mixed effects models and surveys recent advances and applications.
引用
收藏
页码:387 / 419
页数:33
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