Biography
Preface
CHAPTER 1  Intr0dueti0n
  1.1 What is Longitudinal Data Analysis?
  1.2 History of Longitudinal Analysis and its Progress
  1.3 Longitudinal Data Structures
    1.3.1 Multivariate Data Structure
    1.3.2 Univariate Data Structure
    1.3.3 Balanced and Unbalanced Longitudinal Data
  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
    1.8.1 Randomized Controlled Clinical Trial on the Effectiveness of Acupuncture Treatment on PTSD
    1.8.2 Asset and Health Dynamics Among the Oldest Old (AHEAD)
CHAPTER 2 Traditional Methods of Longitudinal Data Analysis..
  2.1 Descriptive Approaches
    2.1.1 Time Plots of Trends
    2.1.2 Paired t-Test
    2.1.3 Effect Size Between Two Means and its Confidence Interval
    2.1.4 Empirical Illustration: Descriptive Analysis on the Effectiveness of Acupuncture Treatment in Reduction of PTSD Symptom Severity
  2.2 Repeated Measures ANOVA
    2.2.1 Specifications of One-Factor ANOVA
    2.2.2 One-Factor Repeated Measures ANOVA
    2.2.3 Specifications of Two-Factor Repeated Measures ANOVA
    2.2.4 Empirical Illustration: A Two-Factor Repeated Measures ANOVA - The Effectiveness of Acupuncture Treatment on PCL Revisited
  2.3 Repeated Measures MANOVA
    2.3.1 General MANOVA
    2.3.2 Hypothesis Testing on Effects in MANOVA
    2.3.3 Repeated Measures MANOVA
    2.3.4 Empirical Illustration: A Two-Factor Repeated Measures MANOVA on the Effectiveness of Acupuncture Treatment on Two Psychiatric Disorders
  2.4 Summary
CHAPTER 3 Linear Mixed-Effects Models
  3.1 Introduction of Linear Mixed Models: Three Cases
    3.1.1 Case I: One-Factor Linear Mixed Model with Random Intercept
    3.1.2 Case II: linear Mixed Model with Random Intercept and Random Slope
    3.1.3 Case III: Linear Mixed Model with Random Effects and Three Covariates
  3.2 Formalization of Linear Mixed Models
    3.2.1 General Specification of Linear Mixed Models
    3.2.2 Variance-Covariance Matrix and Intraindividual Correlation
    3.2.3 Formalization of Variance-Covariance Components
  3.3 Inference and Estimation of Fixed Effects In Linear
  Mixed Models
    3.3.1 Maximum Likelihood Methods
    3.3.2 Statistical Inference and Hypothesis Testing on Fixed Effects
    3.3.3 Missing Data
  3.4 Trend Analysis
    3.4.1 Polynomial Time Functions
    3.4.2 Methods to Reduce Collinearity in Polynomial Time Terms
    3.4.3 Numeric Checks on Polynomial Time Functions
  3.5 Empirical Illustrations: Application of Two Linear
  Mixed Models
    3.5.1 Linear Mixed Model on Effectiveness of Acupuncture Treatment on PCL Score
    3.5.2 Linear Mixed Model on Marital Status and Disability Severity in Older Americans
  3.6 Summary
  ……
CHAPTER 4 Restricted Maximum Likelihood and Inference of Random Effects in Linear Mixed Models
CHAPTER 5 Patterns of Residual Covariance Structure
CHAPTER 6 Residual and Influence Diagnostics
CHAPTER 7  Special Topics on Linear Mixed Models
CHAPTER 8 Generalized Linear Mixed Models on Nonlinear Longitudinal Data
CHAPTER 9 Generalized Estimating Equations (GEEs) Models.
CHAPTER 10 Mixed-Effects Regression Model for Binary Longitudinal Data
CHAPTER 11 Mixed-Effects Multinomial Logit Model for Nominal Outcomes
CHAPTER 12 Longitudinal Transition Models for Categorical Response Data
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