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.
"…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. It fairly reflects that literature over the years in dwelling at length on certain computational methods for maximum likelihood estimation. 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. …
--M. J. Crowder, Biometrics, September 1997
Introduction. Nonlinear regression models for individual data. Hierarchical linear models. Hierarchical nonlinear models. Inference based on individual estimates. Inference based on linearization. Nonperametric and semiparametric inference. Bayesian inference. Pharamcokinetic and pharamacodynamic analysis. Analysis of assay data. Further applications. Open problems and discussion. References. Indices.