Putting risk prediction in perspective: relative utility curves.
Risk prediction models based on medical history or results of tests are increasingly common in the cancer literature. An important use of these models is to make treatment decisions on the basis of estimated risk. The relative utility curve is a simple method for evaluating risk prediction in a medical decision-making framework. Relative utility curves have three attractive features for the evaluation of risk prediction models. First, they put risk prediction into perspective because relative utility is the fraction of the expected utility of perfect prediction obtained by the risk prediction model at the optimal cut point. Second, they do not require precise specification of harms and benefits because relative utility is plotted against a summary measure of harms and benefits (ie, the risk threshold). Third, they are easy to compute from standard tables of data found in many articles on risk prediction. An important use of relative utility curves is to evaluate the addition of a risk factor to the risk prediction model. To illustrate an application of relative utility curves, an analysis was performed on previously published data involving the addition of breast density to a risk prediction model for invasive breast cancer.
Baker
Stuart G
SG
Biometry Research Group, Division of Cancer Prevention, National Cancer Institute, Bethesda, MD 20892-7354, USA. sb16i@nih.gov
D001943:Breast Neoplasms / Q000473:pathology; D003661:Decision Support Techniques; D005260:Female; D006801:Humans; D008432:Mathematical Computing; D015233:Models, Statistical; D009369:Neoplasms / Q000628:therapy; D011237:Predictive Value of Tests; D012372:ROC Curve; D018570:Risk Assessment; D012307:Risk Factors