Contents. I Linear Models 21. Preface. viii. Acknowledgments

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1 Contents Preface Acknowledgments viii xi 1 Introduction Correlated response data Repeated measurements Clustered data Spatially correlated data Multivariate data Explanatory variables Types of models Marginal versus mixed-effects models Models in SAS Alternative approaches Some examples Summary features I Linear Models 21 2 Marginal Linear Models - Normal Theory The marginal linear model (LM) Estimation Inference and test statistics Examples Dental growth data Bone mineral density data... 44

2 iv CONTENTS ADEMEX adequacy data MCM2 biomarker data Summary Linear Mixed-Effects Models - Normal Theory The linear mixed-effects (LME) model Features of the LME model Estimation Inference and test statistics Examples Dental growth data - continued Bone mineral density data - continued Estrogen levels in healthy premenopausal women Summary II Nonlinear Models Generalized Linear and Nonlinear Models The generalized linear model (GLIM) Estimation and inference in the univariate case The GLIM for correlated response data Estimation Inference and test statistics Model selection and diagnostics Examples of GLIM s ADEMEX peritonitis infection data Respiratory disorder data Epileptic seizure data Schizophrenia data The generalized nonlinear model (GNLM) Normal-theory nonlinear model (NLM) Estimation Inference and test statistics Examples of GNLM s LDH enzyme leakage data Orange tree data Respiratory disorder data - continued

3 CONTENTS v Epileptic seizure data - continued Computational considerations Model parameterization and scaling Starting values Summary Generalized Linear and Nonlinear Mixed-Effects Models The generalized linear mixed-effects (GLME) model Estimation Comparing different estimators Inference and test statistics Model selection, goodness-of-fit and diagnostics Examples of GLME models Respiratory disorder data - continued Epileptic seizure data - continued Schizophrenia data - continued ADEMEX hospitalization data The generalized nonlinear mixed-effects (GNLME) model Fully parametric GNLME models Normal-theory nonlinear mixed-effects (NLME) model Overcoming modeling limitations in SAS Estimation Comparing different estimators Computational issues - starting values Inference and test statistics Examples of GNLME models Orange tree data - continued Soybean growth data High flux hemodialyzer data Cefamandole pharmacokinetic data Epileptic seizure data - continued Summary III Further Topics Missing Data in Longitudinal Clinical Trials Background

4 vi CONTENTS 6.2 Missing data mechanisms Missing Completely at Random (MCAR) Missing at Random (MAR) Missing Not at Random (MNAR) Dropout mechanisms Ignorable versus non-ignorable dropout Practical issues with missing data and dropout Developing an analysis plan for missing data Methods of analysis under MAR Likelihood-based methods Imputation-based methods Inverse probability of weighting (IPW) Example: A repeated measures ANCOVA Sensitivity analysis under MNAR Selection models Pattern mixture models Shared parameter (SP) models A repeated measures ANCOVA - continued Missing data - case studies Bone mineral density data - continued MDRD study - GFR data Schizophrenia data - continued Summary Additional Topics and Applications Mixed models with non-gaussian random effects ADEMEX peritonitis and hospitalization data Pharmacokinetic applications Theophylline data Phenobarbital data Joint modeling of longitudinal data and survival data ADEMEX study - GFR data and survival IV Appendices 491 A Some useful matrix notation and results 493 A.1 Matrix notation and results...493

5 CONTENTS vii B Additional results on estimation 497 B.1 The different estimators for mixed-effects models B.2 Comparing large sample properties of the different estimators.498 C Datasets 510 C.1 Dental growth data C.2 Bone mineral density data C.3 ADEMEX adequacy data C.4 MCM2 biomarker data C.5 Estrogen hormone data C.6 ADEMEX peritonitis and hospitalization data C.7 Respiratory disorder data C.8 Epileptic seizure data C.9 Schizophrenia data C.10 LDH enzyme leakage data C.11 Orange tree data C.12 Soybean growth data C.13 High flux hemodialyzer data C.14 Cefamandole pharmacokinetic data C.15 MDRD data C.16 Theophylline data C.17 Phenobarbital data C.18 ADEMEX GFR and survival data D Select SAS macros 513 D.1 The GOF Macro D.2 The GLIMMIX_GOF Macro D.3 The CCC Macro D.4 The CONCORR Macro D.5 The COVPARMS Macro D.6 The VECH Macro

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