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.

Davidian, Marie

Preface, 1 Introduction, 2 Nonlinear regression models for individual data, 3 Hierarchical linear models, 4 Hierarchical nonlinear models, 5 Inference based on individual estimates, 6 Inference based on linearization, 7 Nonparametric and semiparametric inference, 8 Bayesian inference, 9 Pharmacokinetic and pharmacodynamic analysis, 10 Analysis of assay data, 11 Further applications, 12 Open problems and discussion, References, Author index, Subject index