  Survival competing risks models are very useful for studying the incidence of
diseases whose occurrence competes with other possible diseases or health
conditions. These models perform properly when working with terminal events,
such as death, that imply the conclusion of the corresponding study. But they
do not allow the treatment of scenarios with non-terminal competing events that
may occur sequentially. Multi-state models are complex survival models. They
focus on pathways defined by the temporal and sequential occurrence of numerous
events of interest and thus they are suitable for connecting competing
non-terminal events as well as to manage other survival scenarios with higher
complexity. We discuss competing risks within the framework of multi-state
models and clarify the usefulness of both models for analysing epidemiological
data. We highlight the power of multi-state models through a real-world study
of recurrent hip fracture from Bayesian inferential methodology.
