Ewout Steyerberg: I enjoyed your paper introducing the concept of decision curve analysis. I particularly like the way that the method can be directly applied to a data set, without needing to get the sort of information normally required for decision analysis, such as patient utilities or drug costs. I have previously published a similar idea, a weighted accuracy metric, 2 and was wondering whether you could comment on the differences between this metric and the decision curve.

Andrew Vickers: Thanks for the reference: This was not something I had previously seen. There are several similarities between your method and ours; in particular, you use the threshold probability from a predictive model both to classify patients as positive or negative and to assign a relative weight to the cost of false negatives versus false positives. I think this supports a point that I have made elsewhere, namely, that what underpins decision curve analysis is triedand-tested decision theory and that elements of our technique can be found in many previously developed decision analytic applications. I think there are 2 key differences between decision curve analysis and your weighted accuracy metric. First, decision curve analysis allows one to vary the threshold probability over an appropriate range. This is important