附註:Includes bibliographical references (pages 523-553) and index.
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 -- App. B. Technical Details for Sensitivity Analysis.
摘要: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.
