title: Characterizing Decision-Analysis Performances of Risk Prediction Models Using ADAPT Curves.
abstract: The area under the receiver operating characteristic curve is a widely used index to characterize the performance of diagnostic tests and prediction models. However, the index does not explicitly acknowledge the utilities of risk predictions. Moreover, for most clinical settings, what counts is whether a prediction model can guide therapeutic decisions in a way that improves patient outcomes, rather than to simply update probabilities.Based on decision theory, the authors propose an alternative index, the "average deviation about the probability threshold" (ADAPT).An ADAPT curve (a plot of ADAPT value against the probability threshold) neatly characterizes the decision-analysis performances of a risk prediction model.Several prediction models can be compared for their ADAPT values at a chosen probability threshold, for a range of plausible threshold values, or for the whole ADAPT curves. This should greatly facilitate the selection of diagnostic tests and prediction models.
authors.affiliation: From the Institute of Epidemiology and Preventive Medicine (W-CL, Y-CW), and Research Center for Genes, Environment and Human Health, College of Public Health, National Taiwan University, Taipei, Taiwan (W-CL).
authors.forename: Wen-Chung
authors.initials: WC
authors.lastname: Lee
authors.forename: Yun-Chun
authors.initials: YC
authors.lastname: Wu
mesh_terms: D019540:Area Under Curve; D003661:Decision Support Techniques; D006801:Humans; D015233:Models, Statistical; D011237:Predictive Value of Tests; D018570:Risk Assessment; D013647:Task Performance and Analysis