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.
Author(s): Geert Verbeke, Geert Molenberghs
Edition: Corrected
Publisher: Springer
Year: 2000
Language: English
Pages: 579
Cover
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Advisors
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Springer series in statistics
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Title
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Copyright
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Preface
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Acknowledgments
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Contents
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1. Introduction
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2. Examples
......Page 27
3. A model for longitudinal data
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4. Exploratory data analysis
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5. Estimation of the marginal model
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6. Inference for the marginal model
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7. Inference for the random effects
......Page 96
8. Fitting linear mixed models with SAS
......Page 112
9. General guidelines for model building
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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
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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
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References
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Index
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Springer series in statistics (continued)
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