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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.
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
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