Table of Contents
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
3.1. Introduction of linear mixed models: three cases
3.2. Formalization of linear mixed models
3.3. Inference and estimation of fixed effects in linear mixed models
3.5. Empirical illustrations: application of two linear mixed models
Chapter 4: Restricted maximum likelihood and inference of random effects in linear mixed models
4.1. Overview of Bayesian inference
4.2. Restricted maximum likelihood estimator
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
Chapter 5: Patterns of residual covariance structure
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
Chapter 6: Residual and influence diagnostics
6.3. Empirical Illustrations on Influence Diagnostics
Chapter 7: Special topics on linear mixed models
7.1. Adjustment of baseline response in longitudinal data analysis
7.2. Misspecification of the assumed distribution of random effects
Chapter 8: Generalized linear mixed models on nonlinear longitudinal data
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
Chapter 9: Generalized estimating equations (GEEs) models
9.1. Basic specifications and inferences of GEEs
9.3. Relationship between marginal and random-effects models
9.4. Empirical illustration: effect of marital status on disability severity in older Americans
Chapter 10: Mixed-effects regression model for binary longitudinal data
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
Chapter 11: Mixed-effects multinomial logit model for nominal outcomes
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
Chapter 12: Longitudinal transition models for categorical response data
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
Chapter 13: Latent growth, latent growth mixture, and group-based models
13.1. Overview of structural equation modeling
13.3. Latent growth mixture model
13.5. Empirical illustration: effect of marital status on ADL count among older Americans revisited
Chapter 14: Methods for handling missing data
14.1. Mathematical definitions of MCAR, MAR, and MNAR
14.2. Methods handling missing at random
Appendix A: Orthogonal polynomials