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Biomarker research is at the forefront of the quest toward personalized medicine. It is hoped that the discovery of new biomarkers will aid in better understanding of disease pathways and in turn will lead to improved stratification of individuals at risk and to disease prevention. This avenue of research is actively pursued in all major domains of modern medicine, including, but not limited to, cardiovascular research (1) and cancer research (2). Risk stratification is usually based on the probability that a person has or will develop the event of interest within a prespecified time interval. These probabilities are usually derived from risk-prediction models that use regression-based approaches as well as nonparametric techniques (3). Published reporting guidelines have outlined what is expected of research reports that describe the impact of novel biomarkers (4) and genetic factors (5).The ability to improve risk-prediction models is one of the key elements used to evaluate new biomarkers. Numerous metrics have been proposed to assess model performance and quantify its improvement. The area under the ROC curve (AUC),2 a frequently reported standard that originated in the diagnostic setting, equals the probability that given any 2 randomly selected individuals, one with an event (a future event in prognostic and a current event in diagnostic applications) and one without an event, the one with the event has a higher predicted risk. The AUC can be visualized as the area under the plot of sensitivity as a function of 1 minus specificity across all risk thresholds. Improvement in model performance can be quantified by using the increase in the AUC when the new biomarker is added to the risk model. An alternative measure of discrimination …

Caution Is Needed in the Interpretation of Added Value of Biomarkers Analyzed in Matched Case Control Studies