Linear mixed models for longitudinal data
Series: Springer Series in StatisticsPublication details: New York : Springer, 2000Description: xxii, 568 p. ; 24 cmISBN: 9780387950273Subject(s): Longitudinal method | Mathematics | Linear models (Statistics)DDC classification: 519.5 Summary: The SAS routines on mixed models have applications in many areas of statistics, especially biostatistics, but the procedures are not well- documented. Based on short courses given by the authors, this book provides practical guidance for SAS users.
Integrative Medicine
| Item type | Current library | Home library | Call number | Status | Date due | Barcode |
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Books
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Library and Information Centre | Library and Information Centre Book section | 519.5 VER (Browse shelf (Opens below)) | Available | 30327 |
Content :
1. Introduction --
2. Examples --
3. A Model for Longitudinal Data --
4. Exploratory Data Analysis --
5. Estimation of the Marginal Model --
6. Inference for the Marginal Model --
7. Inference for the Random Effects --
8. Fitting Linear Mixed Models with SAS --
9. General Guidelines for Model Building --
10. Exploring Serial Correlation --
11. Local Influence for the Linear Mixed Model --
12. The Heterogeneity Model --
13. Conditional Linear Mixed Models --
14. Exploring Incomplete Data --
15. Joint Modeling of Measurements and Missingness --
16. Simple Missing Data Methods --
17. Selection Models --
18. Pattern-Mixture Models --
19. Sensitivity Analysis for Selection Models --
20. Sensitivity Analysis for Pattern-Mixture Models --
21. How Ignorable Is Missing At Random? --
22. The Expectation-Maximization Algorithm --
23. Design Considerations --
24. Case Studies --
The SAS routines on mixed models have applications in many areas of statistics, especially biostatistics, but the procedures are not well- documented. Based on short courses given by the authors, this book provides practical guidance for SAS users.
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