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Nonlinear Models for Repeated Measurement Data (Monographs on Statistics and Applied Probability) |
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Nonlinear Models for Repeated Measurement Data (Monographs on Statistics and Applied Probability)
List Price: $94.95
Our Price: $75.96
Availability: Usually ships in 24 hours
Manufacturer: Chapman & Hall/CRC
Author: Marie Davidian
Binding: Hardcover
Publication Date: 1995-06-01
Publisher: Chapman & Hall/CRC
Label: Chapman & Hall/CRC
Number Of Pages: 360
Features:
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Editorial Review:
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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Customer Reviews
Average Customer Rating: 
excellent for both theory and applications 2008-02-13
Analysis of repeated measurement data is commonplace in clinical trials and there is a great body of literature and books on the repeated measures analysis using linear models. The many fine texts on repeated measure linear models are often found with the term longitudinal data analysis because the repeated measurements are given over time. As Davidian and Giltinan point out htere is a need for nonlinear models in the case of pharmacokinetics trials. The theory is new and is presented here in a text for the first time. The authors provide many real applications from their experience. They present a good mix of theory and applications. Thorough references to the literature are given. Even though the development is new the authors do treat the computational aspects. Software implementation for Bayesian Hierarchical models is also mentioned. SAS macros that have been developed are also covered. The methodology is developed in the first eight chapters with applications and case studies in Chapters 9-11. Chapter 12 provides open problems to interest researchers. It contains useful information for practitioners but requires mathematical sophistication.
great coverage of theory and applications 2000-08-09
Analysis of repeated measurement data is commonplace in clinical trials and there is a great body of literature and books on the repeated measures analysis using linear models. The many fine texts on repeated measure linear models are often found with the term longitudinal data analysis because the repeated measurements are given over time. As Davidian and Giltinan point out htere is a need for nonlinear models in the case of pharmacokinetics trials. The theory is new and is presented here in a text for the first time. The authors provide many real applications from their experience. They present a good mix of theory and applications. Thorough references to the literature are given. Even though the development is new the authors do treat the computational aspects. Software implementation for Bayesian Hierarchical models is also mentioned. SAS macros that have been developed are also covered. The methodology is developed in the first eight chapters with applications and case studies in Chapters 9-11. Chapter 12 provides open problems to interest researchers. It contains useful information for practitioners but requires mathematical sophistication.
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