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Methods and Applications of Longitudinal Data Analysis
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From classical to the most advanced longitudinal methods, a complete guide, empirically illustrated

Table of Contents

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

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