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 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 TOC: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
