Since the publication of the first edition, the authors have solicited feedback from both the instructors who use the book as a text for their courses as well as the researchers who use the book as a resource for their research. Thus, the improved Second Edition of Applied Longitudinal Analysis features many additions and revisions based on the feedback of readers, making it the go-to reference for applied use in public health, epidemiology, and pharmaceutical sciences.

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.

Preface to First Edition.

Acknowledgments.

Part I. Introduction to Longitudinal and Clustered Data.

1. Longitudinal and Clustered Data.

2. Longitudinal Data. Basic Concepts.

Part II. Linear Models for Longitudinal Continuous Data.

3. Overview of Linear Models for Longitudinal Data.

4. Estimation and Statistical Inference.

5. Modelling the Mean: Analyzing Response Profiles.

6. Modelling the Mean: Parametric Curves.

7. Modelling the Covariance.

8. Linear Mixed Effect Models.

9. Fixed Effects versus Random Effects Models.

10. Residual Analyses and Diagnostics.

Part III. Generalized Linear Models for Longitudinal Data.

11. Review of Generalized Linear Models.

12. Marginal Models: Introduction and Overview.

13. Marginal Models: Generalized Estimating Equations (GEE).

14. Generalized Linear Mixed Effects Models.

15. Generalized Linear Mixed Effects Models: Approximate Methods of Estimation.

16. Contrasting Marginal and Mixed Effects Models.

Part IV. Missing Data and Dropout.

17. Missing Data and Dropout: Overview of Concepts and Methods.

18. Missing Data and Dropout: Multiple Imputation and Weighting Methods.

Part V. Advanced Topics for Longitudinal and Clustered Data.

19. Smoothing Longitudinal Data: Semiparametric Regression Models.

20. Sample Size and Power.

21. Repeated Measures and Related Designs.

22. Multilevel Models.

Appendix A. Gentle Introduction to Vectors and Matrices.

Appendix B. Properties of Expectations and Variance.

Appendix C. Critical Points for a 50:50 Mixture of Chi-Squared Distributions.

References.

Index.