It has been long recognized that missing data are the norm rather than the exception when it comes to the analysis of real data. In this chapter we focus on follow-up studies and on the statistical analysis of longitudinal outcomes with missing data. Due to the wide use of longitudinal studies in many different disciplines, there has been a lot of research in the literature on extensions of selection and pattern-mixture models, which can be considered as the two traditional modeling frameworks for handling missing data (Little and Rubin, 2002; Molenberghs and Kenward, 2007), to the longitudinal setting; see for instance, Verbeke and Molenberghs, (2000), Fitzmaurice et al. (2004), and references therein. These models are applied in non-random missing datasettings, i.e., when the probability of missingness may depend on unobserved longitudinal responses, and require defining the joint distribution of the longitudinal and dropout processes.