Models for Discrete Longitudinal Data

1. Introduction

2. Motivating Studies

3. Generalized Linear Models

4. Linear Mixed Models for Gaussian Longitudinal Data

5. Model Families

6. The Strength of Marginal Models

7. Likelihood-based Marginal Models

8. Generalized Estimating Equations

9. Pseudo-Likelihood

10. Fitting Marginal Models with SAS

11. Conditional Models

12. Pseudo-Likehood

13. From Subject-specific to Random-effects Models

14. The Generalized Linear Mixed Model (GLMM)

15. Fitting Generalized Linear Mixed Models with SAS

16. Marginal versus Random-effects Models

17. The Analgesic Trial

18. Ordinal Data

19. The Epilepsy Data

20. Non-linear Models

21. Pseudo-Likelihood for a Hierarchical Model

22. Random-effects Models with Serial Correlation

23. Non-Gaussian Random Effects

24. Joint Continuous and Discrete Responses

25. High-dimensional Joint Models

26. Missing Data Concepts

27. Simple Methods, Direct Likelihood, and Weighted Generalized Estimating Equations

28. Multiple Imputation and the Expectation-Maximization Algorithm

29. Selection Models

30. Pattern-mixture Models

31. Sensitivity Analysis

32. Incomplete Data and SAS

DRM-restrictions

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Nyckelord: MATHEMATICS / Probability & Statistics / General MAT029000