In longitudinal studies subjects are measured repeatedly on one or more response variable, over time. Although the underlying evolution of such response variables is often continuous in time, in practice the measurements are observed at discrete time points. Moreover it is of interest, particularly in longitudinal clinical trials, to test significant differences between the underlying processes of the same response variables for different treatment groups. The underlying longitudinal processes are not observed precisely, as measurements are subject to error. The main advantage of these studies is to be able to distinguish between changes over time within individuals, variability between subjects and pure measurement error. This is only possible because there is data replication on the sequence of measurements for each subject. In longitudinal clinical trials it is also common to observe relevant events, generating time-to-event outcomes. In this work we will focus on single events. When the two observed processes are related, the analysis of the data set should be suited to the specific objectives. We distinguish three situations: if the interest is to analyse the longitudinal outcome response variable with drop-out at the time-to-event; to analyse time-to-event, whilst exploiting correlation with a noisy version of a time-varying risk factor; or to analyse the relationship between the two processes. Joint models assume a full distribution for the joint distribution of longitudinal and time-to-event processes, which includes a description of the relation between the two processes.