Methods and Applications of Longitudinal Data Analysis

Methods and Applications of Longitudinal Data Analysis describes methods for the analysis of longitudinal data in the medical, biological and behavioral sciences. It introduces basic concepts and functions including a variety of regression models, and their practical applications across many areas of research. Statistical procedures featured within the text include:

descriptive methods for delineating trends over timelinear mixed regression models with both fixed and random effectscovariance pattern models on correlated errorsgeneralized estimating equationsnonlinear regression models for categorical repeated measurementstechniques for analyzing longitudinal data with non-ignorable missing observations

Emphasis is given to applications of these methods, using substantial empirical illustrations, designed to help users of statistics better analyze and understand longitudinal data.

Methods and Applications of Longitudinal Data Analysis equips both graduate students and professionals to confidently apply longitudinal data analysis to their particular discipline. It also provides a valuable reference source for applied statisticians, demographers and other quantitative methodologists.

From novice to professional: this book starts with the introduction of basic models and ends with the description of some of the most advanced models in longitudinal data analysisEnables students to select the correct statistical methods to apply to their longitudinal data and avoid the pitfalls associated with incorrect selectionIdentifies the limitations of classical repeated measures models and describes newly developed techniques, along with real-world examples.

1 Introduction
2 Traditional Methods of Longitudinal Data Analysis
3 Linear Mixed-effects Models
4 Restricted Maximum Likelihood and Inference of Random Effects in Linear Mixed Models
5 Patterns of Residual Covariance Structure
6 Residual and Influence Diagnostics
7 Special Topics on Linear Mixed Models
8 Generalized Linear Mixed Models on Nonlinear Longitudinal Data
9 Generalized Estimating Equations Models (GEEs)
10 Mixed-effects Regression Model for Binary Longitudinal Data
11 Mixed-effects Multinomial Logit Model for Nominal Outcomes
12 Longitudinal Transition Models for Categorical Response Data
13 Latent Growth, Latent Growth Mixture, and Group-based Models
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 States among Older Americans
References
Subject Index