Linear Mixed Models for Longitudinal Data

by Geert Verbeke, Geert Molenberghs

Overview

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.

Product Details

ISBN-13:	9781441902993
Publisher:	Springer New York
Publication date:	04/28/2009
Series:	Springer Series in Statistics
Edition description:	2000
Pages:	570
Product dimensions:	6.10(w) x 9.20(h) x 1.60(d)

Table of Contents

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

Editorial Reviews

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)

From the Publisher