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Preface xvii Preface to First Edition xxi Acknowledgments xxv Part I. Introduction to Longitudinal and Clustered Data 1. Longitudinal and Clustered Data 1 2. Longitudinal Data. Basic Concepts 19 Part II. Linear Models for Longitudinal Continuous Data 3. Overview of Linear Models for Longitudinal Data 49 4. Estimation and Statistical Inference 89 5. Modelling the Mean: Analyzing Response Profiles 105 6. Modelling the Mean: Parametric Curves 143 7. Modelling the Covariance 165 8. Linear Mixed Effect Models 189 9. Fixed Effects versus Random Effects Models 241 10. Residual Analyses and Diagnostics 265 Part III. Generalized Linear Models for Longitudinal Data 11. Review of Generalized Linear Models 291 12. Marginal Models: Introduction and Overview 341 13. Marginal Models: Generalized Estimating Equations (GEE) 353 14. Generalized Linear Mixed Effects Models 395 15. Generalized Linear Mixed Effects Models: Approximate Methods of Estimation 441 16. Contrasting Marginal and Mixed Effects Models 473 Part IV. Missing Data and Dropout 17. Missing Data and Dropout: Overview of Concepts and Methods 489 18. Missing Data and Dropout: Multiple Imputation and Weighting Methods 515 Part V. Advanced Topics for Longitudinal and Clustered Data 19. Smoothing Longitudinal Data: Semiparametric Regression Models 553 20. Sample Size and Power 581 21. Repeated Measures and Related Designs 611 22. Multilevel Models 627 Appendix A. Gentle Introduction to Vectors and Matrices 655 Appendix B. Properties of Expectations and Variance 665 Appendix C. Critical Points for a 50:50 Mixture of Chi-Squared Distributions 669 References 671 Index 695
Garrett M. Fitzmaurice, ScD, is Professor in the Departmentof Biostatistics at the Harvard School of Public Health andDirector of the Laboratory for Psychiatric Biostatistics at McLeanHospital. A Fellow of the American Statistical Association andadvisor for the Wiley Series in Probability and Statistics, Dr.Fitzmaurice's areas of research interest include statisticalmethods for analyzing discrete longitudinal data and methods forhandling missing data. Nan M. Laird, PhD, is Professor of Biostatistics at theHarvard School of Public Health. A Fellow of the AmericanStatistical Association and Institute of Mathematical Sciences, shehas published extensively in the areas of statistical genetics,longitudinal studies, missing or incomplete data, and analysis ofmultiple informant data. James H. Ware, PhD, is Frederick Mosteller Professor ofBiostatistics at the Harvard School of Public Health. A Fellow ofthe American Statistical Association and statistical consultant tothe New England Journal of Medicine, he has made significantcontributions to the development of statistical methods for thedesign and analysis of longitudinal studies.
The text is well-organized and clearly written. It isaccessible to researchers with varying levels of statisticalexpertise, with plenty of data examples that make reading andlearning enjoyable. I recommend it to biostatisticians as well asto clinicians and other health researchers who may not have muchstatistical training ... Applied Longitudinal Analysisisgenerally my first recommendation when asked for a valuableresource in the field due to the breadth of topics covered and itspractical utility. (Journal of BiopharmaceuticalStatistics, 1 January 2013) The book also serves as a valuable reference forresearchers and professionals in the medical, public health, andpharmaceutical fields, as well as those in social and behavioralsciences who would like to learn more about analysing longitudinaldata. (Zentralblatt MATH, 2012)"This book provides very broad coverage of modern methods forlongitudinal data analysis from an applied perspective ... I highlyrecommend this book to statisticians and quantitative researcherswho encounter longitudinal and/or clustered data. In addition, Ithink the book would be an excellent choice as the primary textbookin an applied longitudinal data course." (Journal ofBiopharmaceutical Statistics, 2013)