The aim of this presentation is to introduce joint modelling techniques for the simultaneous analysis of longitudinal time-varying data and time-to-event data. This is an increasing area of interest for the analysis of complex systems. Among others, three main advantages of this approach are: a) it corrects the bias derived from a traditional separate analysis, b) the modelization allows to incorporate and model the between and within correlation among observations and, c) true longitudinal profiles for endogenous covariates can be included in the relative hazard survival sub-model. The relevant benefit of these models is being able to estimate the effect of each subject-specific longitudinal profile in the hazard function for the event of interest, in an adaptive manner. In particular, subject-specific dynamic predictions, like conditional survival functions given the available longitudinal information, can be derived. In order to implement joint models, existing open source libraries in R will be introduced and some illustrations will be given.