- Cover
- Title page
- Table of Contents
- Copyright
- Biography
- Preface
- Chapter 1: Introduction
- Abstracts
- 1.1. What is longitudinal data analysis?
- 1.2. History of longitudinal analysis and its progress
- 1.3. Longitudinal data structures
- 1.4. Missing data patterns and mechanisms
- 1.5. Sources of correlation in longitudinal processes
- 1.6. Time scale and the number of time points
- 1.7. Basic expressions of longitudinal modeling
- 1.8. Organization of the book and data used for illustrations
- Chapter 2: Traditional methods of longitudinal data analysis
- Chapter 3: Linear mixed-effects models
- Chapter 4: Restricted maximum likelihood and inference of random effects in linear mixed models
- Abstract
- 4.1. Overview of Bayesian inference
- 4.2. Restricted maximum likelihood estimator
- 4.3. Computational procedures
- 4.4. Approximation of random effects in linear mixed models
- 4.5. Hypothesis testing on variance component G
- 4.6. Empirical illustrations: linear mixed models with REML
- 4.7. Summary
- Chapter 5: Patterns of residual covariance structure
- Abstract
- 5.1. Residual covariance pattern models with equal spacing
- 5.2. Residual covariance pattern models with unequal time intervals
- 5.3. Comparison of covariance structures
- 5.4. Scaling of time as a classification factor
- 5.5. Least squares means, local contrasts, and local tests
- 5.6. Empirical illustrations: estimation of two linear regression models
- 5.7. Summary
- Chapter 6: Residual and influence diagnostics
- Chapter 7: Special topics on linear mixed models
- Chapter 8: Generalized linear mixed models on nonlinear longitudinal data
- Abstract
- 8.1. A brief overview of generalized linear models
- 8.2. Generalized linear mixed models and statistical inferences
- 8.3. Methods of estimating parameters in generalized linear mixed models
- 8.4. Nonlinear predictions and retransformation of random components
- 8.5. Some popular specific generalized linear mixed models
- 8.6. Summary
- Chapter 9: Generalized estimating equations (GEEs) models
- Chapter 10: Mixed-effects regression model for binary longitudinal data
- Abstract
- 10.1. Overview of Conventional Logistic and Probit Regression Models
- 10.2. Specification of Random Intercept Logistic Regression Model
- 10.3. Specification of Random Coefficient Logistic Regression Model
- 10.4. Inference of Mixed-Effects Logit Model
- 10.5. Approximation of Variance for Predicted Response Probability
- 10.6. Interpretability of Regression Coefficients and Odds Ratios
- 10.7. Computation of Conditional Effect and Conditional Odds Ratio for a Covariate
- 10.8. Empirical Illustration: Effect of Marital Status on Probability of Disability Among Older Americans
- 10.9. Summary
- Chapter 11: Mixed-effects multinomial logit model for nominal outcomes
- Abstract
- 11.1. Overview of multinomial logistic regression model
- 11.2. Mixed-effects multinomial logit models and nonlinear predictions
- 11.3. Estimation of fixed and random effects
- 11.4. Approximation of variance–covariance matrix on probabilities
- 11.5. Conditional effects of covariates on probability scale
- 11.6. Empirical illustration: marital status and longitudinal trajectories of disability and mortality among older Americans
- 11.7. Summary
- Chapter 12: Longitudinal transition models for categorical response data
- Abstract
- 12.1. Overview of two-time multinomial transition modeling
- 12.2. Longitudinal transition models with only fixed effects
- 12.3. Mixed-effects multinomial logit transition models
- 12.4. Empirical illustration: predicted transition probabilities in functional status and marital status among older Americans
- 12.5. Summary
- Chapter 13: Latent growth, latent growth mixture, and group-based models
- Chapter 14: Methods for handling missing data
- Appendix A: Orthogonal polynomials
- Appendix B: The delta method
- Appendix C: Quasi-likelihood functions and properties
- Appendix D: Model specification and SAS program for random coefficient multinomial logit model on health state among older Americans
- References
- Subject Index
Methods and Applications of Longitudinal Data Analysis
Xian Liu
Uniformed Services University of the Health Sciences
DoD Deployment Health Clinical Center, Defense Centers of Excellence, Walter Reed National Military Medical Center
DoD Deployment Health Clinical Center, Defense Centers of Excellence, Walter Reed National Military Medical Center
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