Linear Mixed Models for Longitudinal Data

✍ Scribed by Geert Verbeke, Geert Molenberghs

• Publisher: Springer
• Year: 2000
• Tongue: English
• Leaves: 579
• Edition: Corrected
• Category: Library

✦ Synopsis

This book provides a comprehensive treatment of linear mixed models for continuous longitudinal data. Next to model formulation, this edition puts major emphasis on exploratory data analysis for all aspects of the model, such as the marginal model, subject-specific profiles, and residual covariance structure. Further, model diagnostics and missing data receive extensive treatment. Sensitivity analysis for incomplete data is given a prominent place.Most analyses were done with the MIXED procedure of the SAS software package, but the data analyses are presented in a software-independent fashion.

✦ Table of Contents

Cover
......Page 1
Advisors
......Page 2
Springer series in statistics
......Page 3
Title
......Page 4
Copyright
......Page 5
Preface
......Page 6
Acknowledgments
......Page 8
Contents
......Page 10
1. Introduction
......Page 22
2. Examples
......Page 27
3. A model for longitudinal data
......Page 39
4. Exploratory data analysis
......Page 50
5. Estimation of the marginal model
......Page 60
6. Inference for the marginal model
......Page 74
7. Inference for the random effects
......Page 96
8. Fitting linear mixed models with SAS
......Page 112
9. General guidelines for model building
......Page 140
10. Exploring serial correlation
......Page 154
11. Local inference for the linear mixed model
......Page 170
12. The heterogeneity model
......Page 187
13. Conditional linear mixed models
......Page 206
14. Exploring incomplete data
......Page 218
15. Joint modeling of measurements and missingness
......Page 225
16. Simple missing data methods
......Page 236
17. Selection models
......Page 245
18. Pattern-mixture models
......Page 288
19. Sensitivity analysis for selection models
......Page 307
20. Sensitivity analysis for pattern-mixture models
......Page 343
21. How ignorable is missing at random?
......Page 387
22. The expectation–maximization algorithm
......Page 399
23. Design considerations
......Page 403
24. Case studies
......Page 417
Appendix a: software
......Page 497
Appendix b: technical details for sensitivity analysis
......Page 526
References
......Page 533
Index
......Page 564
Springer series in statistics (continued)
......Page 579