Decision curve analysis is a method to evaluate prediction models and diagnostic tests that was introduced by Vickers and Elkin in a 2006 publication in Medical Decision Making.
The method sought to overcome the limitations of both traditional statistical metrics, such as discrimination and calibration, which are not directly informative as to clinical value, and full decision analytic approaches, which are too unwieldy to be used in regular biostatistical practice.
Decision curve analysis showed that the prediction nomogram of Han et al. for spontaneous intracerebral hemorrhage was clinically useful. The study delineates a predictive nomogram combining clinical and imaging features, which can help identify patients who may die 1).