Linear Mixed Models for Longitudinal Data (Springer Series in Statistics)

Verbeke, Geert; Molenberghs, Geert

ISBN 10: 1441902996 / ISBN 13: 9781441902993

Edité par Springer (edition 2000), 2009

A propos de cet article

It's a well-cared-for item that has seen limited use. The item may show minor signs of wear. All the text is legible, with all pages included. It may have slight markings and/or highlighting.

N° de réf. du vendeur 1441902996-11-1

Détails bibliographiques

• Titre: Linear Mixed Models for Longitudinal Data (Springer Series in Statistics)
• Éditeur: Springer (edition 2000)
• Date de parution: 2009
• Langue: anglais
• ISBN à 10 chiffres: 1441902996
• ISBN à 13 chiffres: 9781441902993
• Reliure: Paperback
• Édition: 2000.
• État de l'article: Very Good

A propos de ce titre

Synopsis

The SAS routines on mixed models have applications in many areas of statistics, especially biostatistics, but the procedures are not well- documented. Based on short courses given by the authors, this book provides practical guidance for SAS users.

Présentation de l'éditeur

This book provides a comprehensive treatment of linear mixed models for continuous longitudinal data. Next to model formulation, this edition puts major emphasis on exploratory data analysis for all aspects of the model, such as the marginal model, subject-specific profiles, and residual covariance structure. Further, model diagnostics and missing data receive extensive treatment. Sensitivity analysis for incomplete data is given a prominent place. Several variations to the conventional linear mixed model are discussed (a heterogeity model, condional linear mid models). This book will be of interest to applied statisticians and biomedical researchers in industry, public health organizations, contract research organizations, and academia. The book is explanatory rather than mathematically rigorous. Most analyses were done with the MIXED procedure of the SAS software package, and many of its features are clearly elucidated. How3ever, some other commercially available packages are discussed as well. Great care has been taken in presenting the data analyses in a software-independent fashion. Geert Verbeke is Assistant Professor at the Biostistical Centre of the Katholieke Universiteit Leuven in Belgium. He received the B.S. degree in mathematics (1989) from the Katholieke Universiteit Leuven, the M.S. in biostatistics (1992) from the Limburgs Universitair Centrum, and earned a Ph.D. in biostatistics (1995) from the Katholieke Universiteit Leuven. Dr. Verbeke wrote his dissertation, as well as a number of methodological articles, on various aspects of linear mixed models for longitudinal data analysis. He has held visiting positions at the Gerontology Research Center and the Johns Hopkins University. Geert Molenberghs is Assistant Professor of Biostatistics at the Limburgs Universitair Centrum in Belgium. He received the B.S. degree in mathematics (1988) and a Ph.D. in biostatistics (1993) from the Universiteit Antwerpen. Dr. Molenberghs published methodological work on the analysis of non-response in clinical and epidemiological studies. He serves as an associate editor for Biometrics, Applied Statistics, and Biostatistics, and is an officer of the Belgian Statistical Society. He has held visiting positions at the Harvard School of Public Health.