Applied Longitudinal Analysis (eBook)

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 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

"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)