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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This review is from: Nonlinear Models for Repeated Measurement Data (Chapman & Hall/CRC Monographs on Statistics & Applied Probability) (Hardcover)
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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First Sentence:
Data consisting of repeated measurements taken on each of a number of individuals arise commonly in biological and biomedical applications. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
hierarchical nonlinear model, quinidine data, relaxin bioassay, pup weight data, parametric model specification, cumulative bole volume, average leaf weight, argatroban concentration, indomethacin data, calibration inference, individual regression parameters, squares line superimposed, nonlinear hierarchy, variance function estimation, individual mean response, power variance function, restricted maximum likelihood approach, ith litter, nonlinear mixed effects models, repeated measurement data, pharmacokinetics literature, normal theory maximum likelihood, estimated random effects, mean response function, pooled estimation Key Phrases - Capitalized Phrases (CAPs): (learn more)
Monte Carlo, Akaike Information Criterion, Parameter Estimate of Population Mean, Plant Introduction New!
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Data consisting of repeated measurements taken on each of a number of individuals arise commonly in biological and biomedical applications. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
hierarchical nonlinear model, quinidine data, relaxin bioassay, pup weight data, parametric model specification, cumulative bole volume, average leaf weight, argatroban concentration, indomethacin data, calibration inference, individual regression parameters, squares line superimposed, nonlinear hierarchy, variance function estimation, individual mean response, power variance function, restricted maximum likelihood approach, ith litter, nonlinear mixed effects models, repeated measurement data, pharmacokinetics literature, normal theory maximum likelihood, estimated random effects, mean response function, pooled estimation Key Phrases - Capitalized Phrases (CAPs): (learn more)
Monte Carlo, Akaike Information Criterion, Parameter Estimate of Population Mean, Plant Introduction New!
Books on Related Topics | Concordance | Text Stats Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
Citations (learn more)
This book cites 78
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89
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- The Analysis of Contingency Tables, Second Edition (Chapman & Hall/CRC Monographs on Statistics & Applied Probability) by Brian S. Everitt in Front Matter (1), and Front Matter (2)
- Subset Selection in Regression, Second Editon by Alan Miller in Front Matter (1), and Front Matter (2)
- Analysis of Repeated Measures (Chapman & Hall/CRC Monographs on Statistics & Applied Probability) by Martin J. Crowder in Front Matter, and Back Matter
- Density Estimation for Statistics and Data Analysis (Chapman & Hall/CRC Monographs on Statistics & Applied Probability) by Bernard. W. Silverman in Front Matter, and Back Matter
- Multidimensional Scaling, Second Edition by Trevor F. Cox in Front Matter (1), and Front Matter (2)
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- Statistical Computing: An Introduction to Data Analysis using S-Plus by Michael J. Crawley on page 430, and Back Matter
- Data Analysis and Graphics Using R: An Example-based Approach (Cambridge Series in Statistical and Probabilistic Mathematics) by John Maindonald on page 347, and Back Matter
- Variance Components: Mixed Models, Methodologies and Applications (Chapman & Hall/CRC Monographs on Statistics & Applied Probability) by Poduri S.R.S. Rao in Front Matter, and Back Matter
- Deterministic And Stochastic Models Of Aids Epidemics And Hiv Infections With Intervention by Tan Wai-Yuan on page 381, and page 566
- Statistical Evidence: A Likelihood Paradigm (Chapman & Hall/CRC Monographs on Statistics & Applied Probability) by Richard Royall in Front Matter
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