When the development over time is analysed in a particular continuous outcome variable, it is quite common that the variable reaches either a ceiling or a floor. When floor or ceiling effects occurs, standard regression-based methods are not suitable for the longitudinal data analysis. To analyse outcome variables with floor or ceiling effects, two-part models can be used. In Chapter 9 it is explained that a distinction can be made between the standard two-part models and the joint two-part models. For the standard two-part models, the process which is studied is seen as two separate processes. One process reaching the floor or ceiling or not and one process for the observations not reaching the floor or ceiling. For the joint two-part models the process which is studied is seen as one process. When the outcome variable has a latent normal distribution, tobit mixed model analysis can be used. A latent normal distribution means that the outcome variable has a normal distribution, but part of that normal distribution cannot be measured and will have the same (floor or ceiling) value for all observations. The advantage of a tobit mixed model analysis is that the regression coefficient of the analysis has the same interpretation as the regression coefficient obtained from a standard mixed model analysis .Although tobit mixed model analysis is not used extensively in medical studies, it has very nice features. Also in this chapter, all methods are accompanied by extensive real-life data examples.