Chains of treatment epizodes and individual clinical pathways of patients will be explored. The RWD module uses primarily retrospective data. Sequential of semi-automated algorithms steps (Determination of health states – Selection of patients & treatment epizodes – Data Transformation – Deriving modell structure – Populating the modell with data) will be used to generate health states and transition probabilities for health-economic (e.g. Markov) models. The long-term follow-up of patients provides an opportunity to map real-life care practices and to explore the effectiveness of alternative treatment forms (such as radiotherapy, surgery, hormone therapy, chemotherapy, etc.), as well as the cost-effectiveness of procedures. Within the project, the individual genetic background of prostate cancer patients will be mapped using new generation sequencing techniques. The resulting genetic and gene expression patterns will serve as inputs for estimating cost-effectiveness on a personalized basis.