This book provides a comprehensive treatment of linear mixed models forcontinuous longitudinal data. Next to model formulation this edition putsmajor emphasis o... » 

This book provides a comprehensive treatment of linear mixed models forcontinuous longitudinal data. Next to model formulation this edition putsmajor emphasis on exploratory data analysis for all aspects of the model suchas the marginal model subjectspecific profiles and residual covariancestructure. Further model diagnostics and missing data receive extensivetreatment. Sensitivity analysis for incomplete data is given a prominent place.Several variations to the conventional linear mixed model are discussed aheterogeity model conditional linear mid models. This book will be ofinterest to applied statisticians and biomedical researchers in industrypublic health organizations contract research organizations and academia. Thebook is explanatory rather than mathematically rigorous. Most analyses weredone with the MIXED procedure of the SAS software package and many of itsfeatures are clearly elucidated. How3ever some other commercially availablepackages are discussed as well. Great care has been taken in presenting thedata analyses in a softwareindependent fashion. TOCIntroduction. Examples. Amodel for Longitudinal Data. Exploratory Data Analysis. Estimation of theMarginal Model. Inference for the Marginal Model. Inference for the RandomEffects. Fitting Linear Mixed Models with SAS. General Guidelines for ModelBuilding. Exploring Serial Correlation. Local Influence for the Linear MixedModel. The Heterogeneity Model. Conditional Linear Mixed Models. ExploringIncomplete Data. Joint Modeling of Measurements and Missingness. Simple MissingData Methods. Selection Models. Pattern Mixture Models. Sensitivity Analysisfor Selection Models. Sensitivity Analysis for Models. How Ignorable is Missingat Random?. The ExpectationMaximization Algorithm. Design Considerations. CaseStudies. «