Linear mixed models for longitudinal data

Geert Verbeke, Geert Molenberghs - Collection Springer Series In Statistics

570 pages, parution le 20/04/2004

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Résumé

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 GerontologyResearch 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.

Sommaire

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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Caractéristiques techniques

	PAPIER
Éditeur(s)	Springer
Auteur(s)	Geert Verbeke, Geert Molenberghs
Collection	Springer Series In Statistics
Parution	20/04/2004
Nb. de pages	570
Format	16 x 24
Couverture	Relié
Poids	929g
Intérieur	Noir et Blanc
EAN13	9780387950273
ISBN13	978-0-387-95027-3