TY - JOUR
T1 - Statistical methods for the assessment of prognostic biomarkers(part II): calibration and re-classification
AU - Tripepi, Giovanni
AU - Jager, Kitty J.
AU - Dekker, Friedo W.
AU - Zoccali, Carmine
PY - 2010
Y1 - 2010
N2 - 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
AB - 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
U2 - https://doi.org/10.1093/ndt/gfq046
DO - https://doi.org/10.1093/ndt/gfq046
M3 - Article
C2 - 20167948
SN - 0931-0509
VL - 25
SP - 1402
EP - 1405
JO - Nephrology, dialysis, transplantation
JF - Nephrology, dialysis, transplantation
IS - 5
ER -