Praise for the First Edition
’. . . [this book] should be on the shelf of everyone interested
in . . . longitudinal data analysis.’
–Journal of the American Statistical Association
Features newly developed topics and applications of the
analysis of longitudinal data
Applied Longitudinal Analysis, Second Edition presents
modern methods for analyzing data from longitudinal studies and now
features the latest state-of-the-art techniques. The book
emphasizes practical, rather than theoretical, aspects of methods
for the analysis of diverse types of longitudinal data that can be
applied across various fields of study, from the health and medical
sciences to the social and behavioral sciences.
The authors incorporate their extensive academic and research
experience along with various updates that have been made in
response to reader feedback. The Second Edition features six newly
added chapters that explore topics currently evolving in the field,
including:
* Fixed effects and mixed effects models
* Marginal models and generalized estimating equations
* Approximate methods for generalized linear mixed effects
models
* Multiple imputation and inverse probability weighted
methods
* Smoothing methods for longitudinal data
* Sample size and power
Each chapter presents methods in the setting of applications to
data sets drawn from the health sciences. New problem sets have
been added to many chapters, and a related website features sample
programs and computer output using SAS, Stata, and R, as well as
data sets and supplemental slides to facilitate a complete
understanding of the material.
With its strong emphasis on multidisciplinary applications and
the interpretation of results, Applied Longitudinal
Analysis, Second Edition is an excellent book for courses on
statistics in the health and medical sciences at the
upper-undergraduate and graduate levels. 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 analyzing longitudinal data.
Spis treści
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
O autorze
Garrett M. Fitzmaurice, Sc D, is Professor in the Department
of Biostatistics at the Harvard School of Public Health and
Director of the Laboratory for Psychiatric Biostatistics at Mc Lean
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, Ph D, 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, Ph D, 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.
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