OBJECTIVES:  In the last decade, there are more and more novel statistical approaches applied in practice. One of these is the so-called multi-state modelling (MSM). In this statistical tool the advantages of graphical visualisation of patient pathways from one state to another and the classic survival analysis are merged. The main aim of this study is to demonstrate a straightforward way of presenting results based on simulations from MSM in the field of oncology. METHODS:  The study was based on the nationwide database of the Hungarian National Health Insurance Fund (NHIF). The database includes patient-level information on all reimbursed health care services in Hungary as e.g. in-/outpatient care and medications. An important limitation of the database was that the clinical results (e.g.: lab test results) or disease status were not directly available. MSM-s including such states as first diagnosis of cancer, different metastatic stages and death were fitted to prospective longitudinal data between 01/01/2005 and 31/12/2015. After the model fit a comparative simulation study was carried out assuming different scenarios of parameter settings. RESULTS: Usually, MSM estimates, even in the case of few transitions, can only be summarized in a rather complex structure and their interpretation is often very complicated. However, simulations based on a given structure are relatively easy and fast to carry out. The distribution of survival times from the start of a disease to death (or another states) were calculated and transformed into well-known utility indicators (e.g. QALY, DALY, LYG) and health-related costs for a comparison. CONCLUSIONS: MSM can serve as appropriate and flexible framework for different types of time-dependent Markov chain models used in multiple cohort simulations. These models are essential in support of planning interventions of the healthcare and public health systems at population level e.g. in oncology screening and care.