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Verbeke, Geert

Linear mixed models for longitudinal data - New York : Springer, 2000 - xxii, 568 p. ; 24 cm. - Springer Series in Statistics .

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.

ISBN: 9780387950273

Subjects--Topical Terms:
Longitudinal method
Mathematics
Linear models (Statistics)

Dewey Class. No.: 519.5 / VER