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by Xian Liu
Methods and Applications of Longitudinal Data Analysis
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
Abstract
2.1. Descriptive approaches
2.2. Repeated measures ANOVA
2.3. Repeated measures MANOVA
2.4. Summary
Chapter 3: Linear mixed-effects models
Abstract
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
3.4. Trend analysis
3.5. Empirical illustrations: application of two linear mixed models
3.6. Summary
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
Abstract
6.1. Residual Diagnostics
6.2. Influence Diagnostics
6.3. Empirical Illustrations on Influence Diagnostics
6.4. Summary
Chapter 7: Special topics on linear mixed models
Abstract
7.1. Adjustment of baseline response in longitudinal data analysis
7.2. Misspecification of the assumed distribution of random effects
7.3. Pattern-mixture modeling
7.4. Summary
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
Abstract
9.1. Basic specifications and inferences of GEEs
9.2. Other GEE approaches
9.3. Relationship between marginal and random-effects models
9.4. Empirical illustration: effect of marital status on disability severity in older Americans
9.5. Summary
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
Abstract
13.1. Overview of structural equation modeling
13.2. Latent growth model
13.3. Latent growth mixture model
13.4. Group-based model
13.5. Empirical illustration: effect of marital status on ADL count among older Americans revisited
13.6. Summary
Chapter 14: Methods for handling missing data
Abstract
14.1. Mathematical definitions of MCAR, MAR, and MNAR
14.2. Methods handling missing at random
14.3. Methods handling missing not at random
14.4. Summary
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
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