Nonlinear Models for Repeated Measurement Data

Rating:
Summary: great coverage of theory and applications
Review: 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.