Bayesian inference and Stan offer many advantages for analyzing time-to-event data, including incorporating prior knowledge into the model, propagating all sources of uncertainty to produce well-calibrated predictions, and integrating arbitrary utility functions for optimal decision-making, such as patients' preferences for different types of risks. The latter point is particularly relevant in multistate models where people may differ in their preferences towards multiple competing events. In this workshop, we will briefly introduce Bayesian workflow – the typical steps in Bayesian analysis and basic (single-event) survival models in the Bayesian context. We will then proceed to introduce multistate models where we are tracking multiple event types, such as bleeding and stroke in cardiovascular trials, or stable disease, progressive disease, and death in oncology trials. Time permitting, we will demonstrate how to incorporate the patient’s utility function into a decision analysis.