Applied Longitudinal Analysis 2/e [Fitzmaurice] 9780470380277

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

頁數：730
版次：第2版
年份：2011年
規格：精裝/單色
ISBN：9780470380277

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.