Applied Longitudinal Analysis 2nd Edition by Garrett M Fitzmaurice , Nan M Laird , James H Ware ISBN 0471214876 978-0471214878

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Product details: 

ISBN 10: 0471214876

ISBN 13: 978-0471214878

Author: Garrett M Fitzmaurice , Nan M Laird , James H Ware

A rigorous, systematic presentation of modern longitudinal analysis
Longitudinal studies, employing repeated measurement of subjects over time, play a prominent role in the health and medical sciences as well as in pharmaceutical studies. An important strategy in modern clinical research, they provide valuable insights into both the development and persistence of disease and those factors that can alter the course of disease development.
Written at a technical level suitable for researchers and graduate students, Applied Longitudinal Analysis provides a rigorous and comprehensive description of modern methods for analyzing longitudinal data. Focusing on General Linear and Mixed Effects Models for continuous responses, and extensions of Generalized Linear Models for discrete responses, the authors discuss in detail the relationships among these different models, including their underlying assumptions and relative merits. The book features:
* A focus on practical applications, utilizing a wide range of examples drawn from real-world studies
* Coverage of modern methods of regression analysis for correlated data
* Analyses utilizing SAS(r)
* Multiple exercises and “homework” problems for review
An accompanying Web site features twenty-five real data sets used throughout the text, in addition to programming statements and selected computer output for the examples.

Table of contents: 

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

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