Nonlinear Models for Repeated Measurement Data

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CRC Press, Jun 1, 1995 - Mathematics - 360 pages
Nonlinear measurement data arise in a wide variety of biological and biomedical applications, such as longitudinal clinical trials, studies of drug kinetics and growth, and the analysis of assay and laboratory data. Nonlinear Models for Repeated Measurement Data provides the first unified development of methods and models for data of this type, with a detailed treatment of inference for the nonlinear mixed effects and its extensions. A particular strength of the book is the inclusion of several detailed case studies from the areas of population pharmacokinetics and pharmacodynamics, immunoassay and bioassay development and the analysis of growth curves.
 

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Contents

Preface xiii
8
Nonlinear regression models for individual data
17
Hierarchical linear models
63
Hierarchical nonlinear models 07
97
Inference based on individual estimates
125
Inference based on linearization
151
Nonparametric and semiparametric inference
191
Bayesian inference
217
Pharmacokinetic and pharmacodynamic analysis
237
Analysis of assay data
275
Further applications
299
Open problems and discussion
327
References
333
Author index
349
Subject index
355
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About the author (1995)

Marie Davidian is an Associate Professor in the Department of Biostatistics at the Harvard School of Public Health.

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