Statistical methods for the assessment of prognostic biomarkers(part II): calibration and re-classification

Calibration is the ability of a prognostic model to correctly estimate the probability of a given event across the whole range of prognostic estimates (for example, 30% probability of death, 40% probability of myocardial infarction, etc.). The key difference between calibration and discrimination is that the latter reflects the ability of a given prognostic biomarker to distinguish a status (died/survived, event/non-event), while calibration measures how much the prognostic estimation of a predictive model matches the real outcome probability (that is, the observed proportion of the event). Re-classification is another measure of prognostic accuracy and it reflects how much a new prognostic biomarker increases the proportion of individuals correctly re-classified as having or not having a given event compared to a previous classification based on an existing prognostic biomarker or predictive model