**Gustavo Soutinho**

EPIUnit, ISPUP and CMAT, Universidade do Minho

*Abstract:*

Multi-state models are a useful way of describing a process in which an individual moves through a number of finite states in continuous time. In these models, one important goal is the modeling of transition rates, which is usually done by studying the relationship between covariates and disease evolution. However, biomedical researchers are also interested in reporting other interpretable results in a simple and summarized manner. These include estimates of predictive probabilities, such as the transition probabilities, occupation probabilities, cumulative incidence functions, and sojourn time distributions.

In the inference of these quantities, it is of major importance to check the Markov assumption that claims that given the present state, the future evolution of the process is independent of the states previously visited and the transition times among them.

In the seminar, some of the most popular R packages will be presented, involving recent contributions that provide answers to all these topics. An illustration of the software usage is included using real data.