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
Linear Mixed Models for Longitudinal Data
โ Scribed by Geert Verbeke, Geert Molenberghs
- Publisher
- Springer
- Year
- 2001
- Tongue
- English
- Leaves
- 579
- Series
- Springer Series in Statistics
- Edition
- 1997. Corr. 2nd.
- Category
- Library
No coin nor oath required. For personal study only.
โฆ Synopsis
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. However, 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.
๐ SIMILAR VOLUMES
<p><P>This paperback edition is a reprint of the 2000 edition.</P><P></P><P>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, suc
This book provides a comprehensive treatment of linear mixed models for continuous longitudinal data. Next to model formulation, this book puts major emphasis on exploratory data analysis for all aspects of the model. Several variations to the conventional linear mixed model are discussed. Most anal
<p>This book provides a new analytical approach for dynamic data repeatedly measured from multiple subjects over time. Random effects account for differences across subjects. Auto-regression in response itself is often used in time series analysis. In longitudinal data analysis, a static mixed effec
<p><p>This book provides a theoretical foundation for the analysis of discrete data such as count and binary data in the longitudinal setup. Unlike the existing books, this book uses a class of auto-correlation structures to model the longitudinal correlations for the repeated discrete data that acc