Nonlinear Models for Repeated Measurement Data (Chapman & Hall/CRC Monographs on Statistics & Applied Probability)
| Author | : | |
| Rating | : | 4.20 (516 Votes) |
| Asin | : | 0412983419 |
| Format Type | : | paperback |
| Number of Pages | : | 360 Pages |
| Publish Date | : | 2016-09-14 |
| Language | : | English |
DESCRIPTION:
excellent for both theory and applications Michael R. Chernick 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 c. Five Stars Great book! Packaged wonderfully.. Great Book The content of this book is good. It explains many statistical methods accurately. However, the quality of the print is not that good. It appears to be cheaply mass produced and has a "photocopy" look to the print (e.g. thick block lettering).
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.. 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
Crowder, Biometrics, September 1997 . There is a leaning toward biopharmaceutical applications, this being a field in which the authors are acknowledged authorities. There are no exercises, but enough detail of the methodology is given, together with helpful guidance on available software, to enable the keen novice to try his hand. Lastly, I hope I'll be forgiven for just adding a couple of citations to those given in the book: a slightly different perspective on nonlinear regression models for repeated measures is described briefly in Crowder and Hand (1990, Section 9.4) and at greater length in Hand and Crowder (1995, chapter 8).--M. J. It fairly reflects that literature over the years in dwelling at length on certain computational methods for maximum likelihood estimation. …gives a very well-written account of the field over the past few decades, focusing mainly on US work and including much of the author's own, plus a glimpse of the future