TY  - GEN
AU  - Verbeke, Geert
AU  - Molenberghs, Geert 
TI  - Linear mixed models for longitudinal data
SN  - 9780387950273
U1  - 519.5 
PY  - 2000///
CY  - New York : 
PB  - Springer, 
KW  - Longitudinal method
KW  - Mathematics
KW  - Linear models (Statistics)
N1  - 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 --
N2  - 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
ER  -