Nonlinear Models for Repeated Measurement Data

ISBN-13: 9780412983412 / Angielski / Twarda / 1995 / 360 str.

Marie Davidian; M. Davidian; David M. Giltinan

Opis

Analysis of repeated measurement data is a recurrent challenge to statisticians engaged in biological and biomedical applications. For example, data from clinical trials are often longitudinal in nature, with repeated measures of response taken over time. In pharmacokinetic studies, serial measurements of drug concentrations are taken from each participant. By definition, growth studies involve repeated measurements over time. In other applications, measurements on experimental units may be repeated across some other dimension, e.g. spatially rather than temporally.

Recenzje

"…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

Spis treści

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.

O autorze

Davidian, Marie