Longitudinal Data Analysis

DONALD HEDEKER, PHD, is Professor of Biostatistics in the Division of Epidemiology and Biostatistics, School of Public Health at the University of Illinois at Chicago. He is a Fellow of the American Statistical Association and the author of numerous peer-reviewed papers. ROBERT D. GIBBONS, PHD, is Director of the Center for Health Statistics; Professor of Biostatistics in the Division of Epidemiology and Biostatistics, School of Public Health; and Professor of Psychiatry in the College of Medicine, all at the University of Illinois at Chicago. He is a Fellow of the American Statistical Association and the author of numerous peer-reviewed papers.

"…a useful resource for students and mathematically inclined practitioners…I would not hesitate to recommend this book…" (Journal of the American Statistical Association, September 2007) "Comparing this book with…other books on this subject…this is certainly one of the foremost." (Journal of Biopharmaceutical Statistics, Vol. 17, Issue 3, 2007)"...this book represents a unique and important contribution to the field of psychology." (PsycCRITIQUES, March 14, 2007)"This innovative classroom-tested book is…highly recommended." (CHOICE, October 2006)

• Preface. Acknowledgments. Acronyms. 1. Introduction. 1.1 Advantages of Longitudinal Studies.1.2 Challenges of Longitudinal Data Analysis.1.3 Some General Notation.1.4 Data Layout.1.5 Analysis Considerations.1.6 General Approaches.1.7 The Simplest Longitudinal Analysis.1.8 Summary.2. ANOVA Approaches to Longitudinal Data.2.1Single-Sample Repeated Measures ANOVA.2.2 Multiple-Sample Repeated Measures ANOVA.2.3 Illustration.2.4 Summary.3. MANOVA Approaches to Longitudinal Data.3.1 Data Layout for ANOVA versus MANOVA.3.2 MANOVA for Repeated Measurements.3.3 MANOVA of Repeated Measures-s Sample Case.3.4 Illustration.3.5 Summary.4. Mixed-Effects Regression Models for Continuous Outcomes.4.1 Introduction.4.2 A Simple Linear Regression Model.4.3 Random Intercept MRM.4.4 Random Intercept and Trend MRM.  4.5 Matrix Formulation.4.6 Estimation .4.7 Summary.5. Mixed-Effects Polynomial Regression Models.5.1 Introduction.5.2 Curvilinear Trend Model.5.3 Orthogonal Polynomials.5.4 Summary.6. Covariance Pattern Models.6.1 Introduction.6.2 Covariance Pattern Models.6.3 Model Selection.6.4 Example.6.5 Summary.7. Mixed Regression Models with Autocorrelated Errors.7.1 Introduction.7.2 MRMs with AC Errors.7.3 Model Selection.7.4 Example.7.5 Summary.8. Generalized Estimating Equations (GEE) Models.8.1 Introduction.8.2 Generalized Linear Models (GLMs).8.3 Generalized Estimating Equations (GEE) Models.8.4 GEE Estimation.8.5 Example.8.6 Summary.9. Mixed-Effects Regression Models for Binary Outcomes.9.1 Introduction.9.2 Logistic Regression Model.9.3 Probit Regression Models.9.4 Threshold Concept.9.5 Mixed-Effects Logistic Regression Model.9.6 Estimation.9.7 Illustration.9.8 Summary.10. Mixed-Effects Regression Models for Ordinal Outcomes.10.1 Introduction.10.2 Mixed-Effects Proportional Odds Model.10.3 Psychiatric Example.10.4 Health Services Research Example.10.5 Summary.11. Mixed-Effects Regression Models for Nominal Data.11.1 Mixed-Effects Multinomial Regression Model.11.2 Health Services Research Example.1 1.3 Competing Risk Survival Models.11.4 Summary.12. Mixed-effects Regression Models for Counts.12.1 Poisson Regression Model.12.2 Modified Poisson Models.12.3 The ZIP Model.12.4 Mixed-Effects Models for Counts.12.5 Illustration.12.6 Summary.13. Mixed-Effects Regression Models for Three-Level Data.13.1 Three-Level Mixed-Effects Linear Regression Model.13.1.1 Illustration.13.2 Three-Level Mixed-Effects Nonlinear Regression Models.13.3 Summary.14. Missing Data in Longitudinal Studies.14.1 Introduction.14.2 Missing Data Mechanisms.14.3 Models and Missing Data Mechanisms.14.4 Testing MCAR.14.5 Models for Nonignorable Missingness.14.6 Summary.Bibliography.Topic Index.