Garrett M. Fitzmaurice, ScD, is Professor in the Department of Biostatistics at the Harvard School of Public Health and Director of the Laboratory for Psychiatric Biostatistics at McLean Hospital. A Fellow of the American Statistical Association and advisor for the Wiley Series in Probability and Statistics, Dr. Fitzmaurice's areas of research interest include statistical methods for analyzing discrete longitudinal data and methods for handling missing data. Nan M. Laird, PhD, is Professor of Biostatistics at the Harvard School of Public Health. A Fellow of the American Statistical Association and Institute of Mathematical Sciences, she has published extensively in the areas of statistical genetics, longitudinal studies, missing or incomplete data, and analysis of multiple informant data.James H. Ware, PhD, is Frederick Mosteller Professor of Biostatistics at the Harvard School of Public Health. A Fellow of the American Statistical Association and statistical consultant to the New England Journal of Medicine, he has made significant contributions to the development of statistical methods for the design and analysis of longitudinal studies.

• Preface xvii Preface to First Edition xxiAcknowledgments xxvPart I. Introduction to Longitudinal and Clustered Data1. Longitudinal and Clustered Data 12. Longitudinal Data. Basic Concepts 19Part II. Linear Models for Longitudinal Continuous Data3. Overview of Linear Models for Longitudinal Data 494. Estimation and Statistical Inference 895. Modelling the Mean: Analyzing Response Profiles 1056. Modelling the Mean: Parametric Curves 1437. Modelling the Covariance 1658. Linear Mixed Effect Models 1899. Fixed Effects versus Random Effects Models 24110. Residual Analyses and Diagnostics 265Part III. Generalized Linear Models for Longitudinal Data11. Review of Generalized Linear Models 29112. Marginal Models: Introduction and Overview 34113. Marginal Models: Generalized Estimating Equations (GEE) 35314. Generalized Linear Mixed Effects Models 39515. Generalized Linear Mixed Effects Models: Approximate Methods of Estimation 44116. Contrasting Marginal and Mixed Effects Models 473Part IV. Missing Data and Dropout17. Missing Data and Dropout: Overview of Concepts and Methods 48918. Missing Data and Dropout: Multiple Imputation and Weighting Methods 515Part V. Advanced Topics for Longitudinal and Clustered Data19. Smoothing Longitudinal Data: Semiparametric Regression Models 55320. Sample Size and Power 58121. Repeated Measures and Related Designs 61122. Multilevel Models 627Appendix A. Gentle Introduction to Vectors and Matrices 655Appendix B. Properties of Expectations and Variance 665Appendix C. Critical Points for a 50:50 Mixture of Chi-Squared Distributions 669References 671Index 695

“The text is well-organized and clearly written. It is accessible to researchers with varying levels of statistical expertise, with plenty of data examples that make reading and learning enjoyable. I recommend it to biostatisticians as well as to clinicians and other health researchers who may not have much statistical training . . . Applied Longitudinal Analysisis generally my first recommendation when asked for a valuable resource in the field due to the breadth of topics covered and its practical utility.”  (Journal of Biopharmaceutical Statistics, 1 January 2013)“The book also serves as a valuable reference for researchers and professionals in the medical, public health, and pharmaceutical fields, as well as those in social and behavioral sciences who would like to learn more about analysing longitudinal data.” (Zentralblatt MATH, 2012)"This book provides very broad coverage of modern methods for longitudinal data analysis from an applied perspective ... I highly recommend this book to statisticians and quantitative researchers who encounter longitudinal and/or clustered data. In addition, I think the book would be an excellent choice as the primary textbook in an applied longitudinal data course." (Journal of Biopharmaceutical Statistics, 2013)

Garrett M. Fitzmaurice, ScD, is Professor in the Department of Biostatistics at the Harvard School of Public Health and Director of the Laboratory for Psychiatric Biostatistics at McLean Hospital. A Fellow of the American Statistical Association and advisor for the Wiley Series in Probability and Statistics, Dr. Fitzmaurice's areas of research interest include statistical methods for analyzing discrete longitudinal data and methods for handling missing data. Nan M. Laird, PhD, is Professor of Biostatistics at the Harvard School of Public Health. A Fellow of the American Statistical Association and Institute of Mathematical Sciences, she has published extensively in the areas of statistical genetics, longitudinal studies, missing or incomplete data, and analysis of multiple informant data.James H. Ware, PhD, is Frederick Mosteller Professor of Biostatistics at the Harvard School of Public Health. A Fellow of the American Statistical Association and statistical consultant to the New England Journal of Medicine, he has made significant contributions to the development of statistical methods for the design and analysis of longitudinal studies.

• Preface xvii Preface to First Edition xxiAcknowledgments xxvPart I. Introduction to Longitudinal and Clustered Data1. Longitudinal and Clustered Data 12. Longitudinal Data. Basic Concepts 19Part II. Linear Models for Longitudinal Continuous Data3. Overview of Linear Models for Longitudinal Data 494. Estimation and Statistical Inference 895. Modelling the Mean: Analyzing Response Profiles 1056. Modelling the Mean: Parametric Curves 1437. Modelling the Covariance 1658. Linear Mixed Effect Models 1899. Fixed Effects versus Random Effects Models 24110. Residual Analyses and Diagnostics 265Part III. Generalized Linear Models for Longitudinal Data11. Review of Generalized Linear Models 29112. Marginal Models: Introduction and Overview 34113. Marginal Models: Generalized Estimating Equations (GEE) 35314. Generalized Linear Mixed Effects Models 39515. Generalized Linear Mixed Effects Models: Approximate Methods of Estimation 44116. Contrasting Marginal and Mixed Effects Models 473Part IV. Missing Data and Dropout17. Missing Data and Dropout: Overview of Concepts and Methods 48918. Missing Data and Dropout: Multiple Imputation and Weighting Methods 515Part V. Advanced Topics for Longitudinal and Clustered Data19. Smoothing Longitudinal Data: Semiparametric Regression Models 55320. Sample Size and Power 58121. Repeated Measures and Related Designs 61122. Multilevel Models 627Appendix A. Gentle Introduction to Vectors and Matrices 655Appendix B. Properties of Expectations and Variance 665Appendix C. Critical Points for a 50:50 Mixture of Chi-Squared Distributions 669References 671Index 695

“The text is well-organized and clearly written. It is accessible to researchers with varying levels of statistical expertise, with plenty of data examples that make reading and learning enjoyable. I recommend it to biostatisticians as well as to clinicians and other health researchers who may not have much statistical training . . . Applied Longitudinal Analysisis generally my first recommendation when asked for a valuable resource in the field due to the breadth of topics covered and its practical utility.”  (Journal of Biopharmaceutical Statistics, 1 January 2013)“The book also serves as a valuable reference for researchers and professionals in the medical, public health, and pharmaceutical fields, as well as those in social and behavioral sciences who would like to learn more about analysing longitudinal data.” (Zentralblatt MATH, 2012)"This book provides very broad coverage of modern methods for longitudinal data analysis from an applied perspective ... I highly recommend this book to statisticians and quantitative researchers who encounter longitudinal and/or clustered data. In addition, I think the book would be an excellent choice as the primary textbook in an applied longitudinal data course." (Journal of Biopharmaceutical Statistics, 2013)

Garrett M. Fitzmaurice, ScD, is Professor in the Department of Biostatistics at the Harvard School of Public Health and Director of the Laboratory for Psychiatric Biostatistics at McLean Hospital. A Fellow of the American Statistical Association and advisor for the Wiley Series in Probability and Statistics, Dr. Fitzmaurice's areas of research interest include statistical methods for analyzing discrete longitudinal data and methods for handling missing data. Nan M. Laird, PhD, is Professor of Biostatistics at the Harvard School of Public Health. A Fellow of the American Statistical Association and Institute of Mathematical Sciences, she has published extensively in the areas of statistical genetics, longitudinal studies, missing or incomplete data, and analysis of multiple informant data.James H. Ware, PhD, is Frederick Mosteller Professor of Biostatistics at the Harvard School of Public Health. A Fellow of the American Statistical Association and statistical consultant to the New England Journal of Medicine, he has made significant contributions to the development of statistical methods for the design and analysis of longitudinal studies.

• Preface xvii Preface to First Edition xxiAcknowledgments xxvPart I. Introduction to Longitudinal and Clustered Data1. Longitudinal and Clustered Data 12. Longitudinal Data. Basic Concepts 19Part II. Linear Models for Longitudinal Continuous Data3. Overview of Linear Models for Longitudinal Data 494. Estimation and Statistical Inference 895. Modelling the Mean: Analyzing Response Profiles 1056. Modelling the Mean: Parametric Curves 1437. Modelling the Covariance 1658. Linear Mixed Effect Models 1899. Fixed Effects versus Random Effects Models 24110. Residual Analyses and Diagnostics 265Part III. Generalized Linear Models for Longitudinal Data11. Review of Generalized Linear Models 29112. Marginal Models: Introduction and Overview 34113. Marginal Models: Generalized Estimating Equations (GEE) 35314. Generalized Linear Mixed Effects Models 39515. Generalized Linear Mixed Effects Models: Approximate Methods of Estimation 44116. Contrasting Marginal and Mixed Effects Models 473Part IV. Missing Data and Dropout17. Missing Data and Dropout: Overview of Concepts and Methods 48918. Missing Data and Dropout: Multiple Imputation and Weighting Methods 515Part V. Advanced Topics for Longitudinal and Clustered Data19. Smoothing Longitudinal Data: Semiparametric Regression Models 55320. Sample Size and Power 58121. Repeated Measures and Related Designs 61122. Multilevel Models 627Appendix A. Gentle Introduction to Vectors and Matrices 655Appendix B. Properties of Expectations and Variance 665Appendix C. Critical Points for a 50:50 Mixture of Chi-Squared Distributions 669References 671Index 695

“The text is well-organized and clearly written. It is accessible to researchers with varying levels of statistical expertise, with plenty of data examples that make reading and learning enjoyable. I recommend it to biostatisticians as well as to clinicians and other health researchers who may not have much statistical training . . . Applied Longitudinal Analysisis generally my first recommendation when asked for a valuable resource in the field due to the breadth of topics covered and its practical utility.”  (Journal of Biopharmaceutical Statistics, 1 January 2013)“The book also serves as a valuable reference for researchers and professionals in the medical, public health, and pharmaceutical fields, as well as those in social and behavioral sciences who would like to learn more about analysing longitudinal data.” (Zentralblatt MATH, 2012)"This book provides very broad coverage of modern methods for longitudinal data analysis from an applied perspective ... I highly recommend this book to statisticians and quantitative researchers who encounter longitudinal and/or clustered data. In addition, I think the book would be an excellent choice as the primary textbook in an applied longitudinal data course." (Journal of Biopharmaceutical Statistics, 2013)