  The recent availability of routine medical data, especially in a
university-clinical context, may enable the discovery of typical healthcare
pathways, i.e., typical temporal sequences of clinical interventions or
hospital readmissions. However, such pathways are heterogeneous in a large
provider such as a university hospital, and it is important to identify similar
care pathways that can still be considered typical pathways. We understand the
pathway as a temporal process with possible transitions from a single initial
treatment state to hospital readmission of different types, which constitutes a
competing risk setting. In this paper, we propose a multi-state model-based
approach to uncover pathway similarity between two groups of individuals. We
describe a new bootstrap procedure for testing the similarity of transition
intensities from two competing risk models with constant transition
intensities. In a large simulation study, we investigate the performance of our
similarity approach with respect to different sample sizes and different
similarity thresholds. The studies are motivated by an application from
urological clinical routine and we show how the results can be transferred to
the application example.
