Evaluating Discrimination of Risk Prediction Models: The C Statistic

Risk prediction models help clinicians develop personalized treatments for patients. The models generally use variables measured at one time point to estimate the probability of an outcome occurring within a given time in the future. It is essential to assess the performance of a risk prediction model in the setting in which it will be used. This is done by evaluating the model's discrimination and calibration. Discrimination refers to the ability of the model to separate individuals who develop events from those who do not. In time-to-event settings, discrimination is the ability of the model to predict who will develop an event earlier and who will develop an event later or not at all. Calibration measures how accurately the model's predictions match overall observed event rates. Here, Pencina and Agostino present a guide to statistics and methods that characterizes the strengths and limitations of the C statistic as a measure of a risk prediction model's ability to discriminate between and predict future events.