Preface.
Acknowledgments.
Acronyms.
1 Introduction.
1.1 Advantages of Longitudinal Studies.
1.2 Challenges of Longitudinal Data Analysis.
1.3 Some General Notation.
1.4 Data Layout.
1.5 Analysis Considerations.
1.6 General Approaches.
1.7 The Simplest Longitudinal Analysis.
1.8 Summary.
2 ANOVA Approaches to Longitudinal Data.
2.1SingleSample Repeated Measures ANOVA.
2.2 MultipleSample Repeated Measures ANOVA.
2.3 Illustration.
2.4 Summary.
3 MANOVA Approaches to Longitudinal Data.
3.1 Data Layout for ANOVA versus MANOVA.
3.2 MANOVA for Repeated Measurements.
3.3 MANOVA of Repeated Measuress Sample Case.
3.4 Illustration.
3.5 Summary.
4 MixedEffects Regression Models for Continuous Outcomes.
4.1 Introduction.
4.2 A Simple Linear Regression Model.
4.3 Random Intercept MRM.
4.4 Random Intercept and Trend MRM.
4.5 Matrix Formulation.
4.6 Estimation .
4.7 Summary.
5 MixedEffects Polynomial Regression Models.
5.1 Introduction.
5.2 Curvilinear Trend Model.
5.3 Orthogonal Polynomials.
5.4 Summary.
6 Covariance Pattern Models.
6.1 Introduction.
6.2 Covariance Pattern Models.
6.3 Model Selection.
6.4 Example.
6.5 Summary.
7 Mixed Regression Models with Autocorrelated Errors.
7.1 Introduction.
7.2 MRMs with AC Errors.
7.3 Model Selection.
7.4 Example.
7.5 Summary.
8 Generalized Estimating Equations (GEE) Models.
8.1 Introduction.
8.2 Generalized Linear Models (GLMs).
8.3 Generalized Estimating Equations (GEE) Models.
8.4 GEE Estimation.
8.5 Example.
8.6 Summary.
9 MixedEffects Regression Models for Binary Outcomes.
9.1 Introduction.
9.2 Logistic Regression Model.
9.3 Probit Regression Models.
9.4 Threshold Concept.
9.5 MixedEffects Logistic Regression Model.
9.6 Estimation.
9.7 Illustration.
9.8 Summary.
10 MixedEffects Regression Models for Ordinal Outcomes.
10.1 Introduction.
10.2 MixedEffects Proportional Odds Model.
10.3 Psychiatric Example.
10.4 Health Services Research Example.
10.5 Summary.
11 MixedEffects Regression Models for Nominal Data.
11.1 MixedEffects Multinomial Regression Model.
11.2 Health Services Research Example.
1 1.3 Competing Risk Survival Models.
11.4 Summary.
12 Mixedeffects Regression Models for Counts.
12.1 Poisson Regression Model.
12.2 Modified Poisson Models.
12.3 The ZIP Model.
12.4 MixedEffects Models for Counts.
12.5 Illustration.
12.6 Summary.
13 MixedEffects Regression Models for ThreeLevel Data.
13.1 ThreeLevel MixedEffects Linear Regression Model.
13.1.1 Illustration.
13.2 ThreeLevel MixedEffects Nonlinear Regression Models.
13.3 Summary.
14 Missing Data in Longitudinal Studies.
14.1 Introduction.
14.2 Missing Data Mechanisms.
14.3 Models and Missing Data Mechanisms.
14.4 Testing MCAR.
14.5 Models for Nonignorable Missingness.
14.6 Summary.
Bibliography.
Topic Index.
