How to use statistical models and methods for clinical prediction

One of the main aims of statistics is to control and model variability in observed phenomena. A second important aim is to translate the results of such modelling into clinical decision-making, e.g., by constructing appropriate prediction models. Currently, model-based individualized predictions play an important role in the era of personalized medicine, where diagnosis and prognosis of a clinical outcome are based on a large number of observed clinical, individual and genetic characteristics (1).