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โœฆ   LIBER   โœฆ

๐Ÿ“

Linear Mixed Models for Longitudinal Data

โœ Scribed by Geert Molenberghs, Geert Verbeke (auth.)


Publisher
Springer-Verlag New York
Year
2000
Tongue
English
Leaves
578
Series
Springer Series in Statistics
Edition
1
Category
Library

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โœฆ Synopsis


This paperback edition is a reprint of the 2000 edition.

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, conditional linear mixed 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 Professor in Biostatistics at the Biostatistical Centre of the Katholieke Universiteit Leuven in Belgium. He is Past President of the Belgian Region of the International Biometric Society, a Board Member of the American Statistical Association, and past Joint Editor of the Journal of the Royal Statistical Society, Series A (2005--2008). He is the director of the Leuven Center for Biostatistics and statistical Bioinformatics (L-BioStat), and vice-director of the Interuniversity Institute for Biostatistics and statistical Bioinformatics (I-BioStat), a joint initiative of the Hasselt and Leuven universities in Belgium.

Geert Molenberghs is Professor of Biostatistics at Universiteit Hasselt and Katholieke Universiteit Leuven in Belgium. He was Joint Editor of Applied Statistics (2001-2004) and Co-Editor of Biometrics (2007-2009). He was President of the International Biometric Society (2004-2005), and has received the Guy Medal in Bronze from the Royal Statistical Society and the Myrto Lefkopoulou award from the Harvard School of Public Health. He is founding director of the Center for Statistics and also the director of the Interuniversity Institute for Biostatistics and statistical Bioinformatics.

Both authors have received the American Statistical Association's Excellence in Continuing Education Award in 2002, 2004, 2005, and 2008. Both are elected Fellows of the American Statistical Association and elected members of the International Statistical Institute.

โœฆ Table of Contents


Front Matter....Pages i-xxii
Introduction....Pages 1-5
Examples....Pages 7-18
A Model for Longitudinal Data....Pages 19-29
Exploratory Data Analysis....Pages 31-40
Estimation of the Marginal Model....Pages 41-54
Inference for the Marginal Model....Pages 55-76
Inference for the Random Effects....Pages 77-92
Fitting Linear Mixed Models with SAS....Pages 93-120
General Guidelines for Model Building....Pages 121-134
Exploring Serial Correlation....Pages 135-150
Local Influence for the Linear Mixed Model....Pages 151-167
The Heterogeneity Model....Pages 169-187
Conditional Linear Mixed Models....Pages 189-200
Exploring Incomplete Data....Pages 201-207
Joint Modeling of Measurements and Missingness....Pages 209-219
Simple Missing Data Methods....Pages 221-229
Selection Models....Pages 231-273
Pattern-Mixture Models....Pages 275-293
Sensitivity Analysis for Selection Models....Pages 295-330
Sensitivity Analysis for Pattern-Mixture Models....Pages 331-374
How Ignorable Is Missing At Random?....Pages 375-386
The Expectation-Maximization Algorithm....Pages 387-390
Design Considerations....Pages 392-404
Case Studies....Pages 405-484
Back Matter....Pages 485-568

โœฆ Subjects


Statistical Theory and Methods


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