part PART I: Introduction and Historical Overview -- chapter 1 Advances in longitudinal data analysis: An historical perspective -- part PART II: Parametric Modeling of Longitudinal Data -- chapter 2 Parametric modeling of longitudinal data: Introduction and overview -- chapter 3 Generalized estimating equations for longitudinal data analysis -- chapter 4 Generalized linear mixed-effects models / chapter 5 Non-linear mixed-effects models / chapter 6 Growth mixture modeling: Analysis with non-Gaussian random effects -- chapter 7 Targets of inference in hierarchical models for longitudinal data -- part PART III: Non-Parametric and Semi-Parametric Methods for Longitudinal Data -- chapter 8 Non-parametric and semi-parametric regression methods: Introduction and overview -- chapter 9 Non-parametric and semi-parametric regression methods for longitudinal data -- chapter 10 Functional modeling of longitudinal data / chapter 11 Smoothing spline models for longitudinal data / chapter 12 Penalized spline models for longitudinal data / part PART IV: Joint Models for Longitudinal Data -- chapter 13 Joint models for longitudinal data: Introduction and overview -- chapter 14 Joint models for continuous and discrete longitudinal data -- chapter 15 Random-effects models for joint analysis of repeated-measurement and time-to-event outcomes -- chapter 16 Joint models for high-dimensional longitudinal data / part PART V: Incomplete Data -- chapter 17 Incomplete data: Introduction and overview / chapter 18 Selection and pattern-mixture models / chapter 19 Shared-parameter models / chapter 20 Inverse probability weighted methods / chapter 21 Multiple imputation / chapter 22 Sensitivity analysis for incomplete data / chapter 23 Estimation of the causal effects of time-varying exposures.