Geert Verbeke, Geert Molenberghs

Linear Mixed Models for Longitudinal Data (eBook, PDF)

• Format: PDF

Produktbeschreibung

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 Editorof 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.

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Produktdetails

• Verlag: Springer US
• Seitenzahl: 570
• Erscheinungstermin: 12. Mai 2009
• Englisch
• ISBN-13: 9781441903006
• Artikelnr.: 44000241

Inhaltsangabe

Introduction Examples A model for Longitudinal Data Exploratory
Data Analysis Estimation of the Marginal Model Inference for the
Marginal Model Inference for the Random Effects Fitting Linear
Mixed Models with SAS General Guidelines for Model Building
Exploring Serial Correlation Local Influence for the Linear Mixed
Model The Heterogeneity Model Conditional Linear Mixed Models
Exploring Incomplete Data Joint Modeling of Measurements and
Missingness Simple Missing Data Methods Selection Models Pattern
-Mixture Models Sensitivity Analysis for Selection Models
Sensitivity Analysis for Models How Ignorable is Missing at Random?
The Expectation-Maximization Algorithm Design Considerations
Case Studies

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Examples.- A Model for Longitudinal Data.- Exploratory Data Analysis.- Estimation of the Marginal Model.- Inference for the Marginal Model.- Inference for the Random Effects.- Fitting Linear Mixed Models with SAS.- General Guidelines for Model Building.- Exploring Serial Correlation.- Local Influence for the Linear Mixed Model.- The Heterogeneity Model.- Conditional Linear Mixed Models.- Exploring Incomplete Data.- Joint Modeling of Measurements and Missingness.- Simple Missing Data Methods.- Selection Models.- Pattern-Mixture Models.- Sensitivity Analysis for Selection Models.- Sensitivity Analysis for Pattern-Mixture Models.- How Ignorable Is Missing At Random?.- The Expectation-Maximization Algorithm.- Design Considerations.- Case Studies.

Rezensionen

From the reviews:

MATHEMATICAL REVIEWS

"This book emphasizes practice rather than mathematical rigor and the majority of the chapters are explanatory rather than research oriented. In this respect, guidance and advice on practical issues are the main focus of the text. Hence it will be of interest to applied statisticians and biomedical researchers in industry, particularly in the pharmaceutical industry, medical public health organizations, contract research organizations, and academia."

"This book provides a comprehensive treatment of linear mixed models for continuous longitudinal data. Over 125 illustrations are included in the book. ... I do believe that the book may serve as a useful reference to a broader audience. Since practical examples are provided as well as discussion of the leading software utilization, it may also be appropriate as a textbook in an advanced undergraduate-level or a graduate-level course in an applied statistics program."(Ana Ivelisse Avil és, Technometrics, Vol. 43 (3), 2001)

"A practical book with a great many examples, including worked computer code and access to the datasets. ... The authors state that the book covers 'linear mixed models for continuous outcomes' ... . The book has four main strengths: its practical bent, its emphasis on exploratory analysis, its description of tools for model checking, and its treatment of dropout and missingness ... . my impression of the book was ... positive. Its strong practical nature and emphasis on dropout modelling are particularly welcome ... ." (Harry Southworth, ISCB Newsletter, June, 2002)

"This book is devoted to linear mixed-effects models with strong emphasis on the SAS procedure. Guidance and advice on practical issues are the main focus of the text. ... It is of value to applied statisticians and biomedical researchers. ... I recommend this book as a reference to applied statisticians and biomedical researchers, particularly in thepharmaceutical industry, medical and public organizations." (Wang Songgui, Zentralblatt MATH, Vol. 956, 2001)