OBJECTIVES: Prostate cancer (PC) is the most common solid neoplasm in Europe. Accurate estimates of prevalence and patient flow through PC states are necessary to analyse disease burden and the health economic impact of novel therapies. This study presents a dynamic patient progression model (PM) estimating the population size of 13 PC clinical states across 8 European countries (EU8; Belgium, France, Germany, Italy, Netherlands, Spain, Sweden, and UK). METHODS: A dynamic PM was developed using a 27-year time horizon to estimate the prevalence and progression of PC from diagnosis through possible death, using a sequence of 13 Markov clinical states (5 non‑metastatic and 8 metastatic). Incidence data were taken from local country registries from 1993 to 2012, and a simple growth model was used to forecast incidence rates to 2020 and beyond. PM flow structure, probabilities of prevalence, progression, and mortality per clinical state were determined using a Delphi panel of PC experts from the EU8 countries and based on published literature and local research. RESULTS: A consensus of expert opinion was reached on the clinical states and flow rates of patients with PC in Europe. This resulted in a refined PM with new clinical states that represent the current treatment paradigm for PC. The PM also estimated growth in prevalence and mortality of future PC patient populations across 13 different clinical states of PC from 2012 to 2020. Five-year prevalence rates from the PM were in agreement with those from the GLOBOCAN 2008 project. CONCLUSIONS: The PM obtained by consensus of expert opinions provided patient prevalence and mortality estimates for 13 distinct PC clinical states. The PM and patient flow rates provide a representative simulation of the local environment, which can be used to assess PC disease burden, health economic impact, and cost of PC care in the EU.