OBJECTIVES: The uncertainty around number needed to treat (NNT) is often represented through a confidence interval (CI). However, it is not clear how the CI can help inform treatment decisions. We developed decision-theoretic measures of uncertainty for the NNT. STUDY DESIGN AND SETTING: We build our argument on the basis that a risk-neutral decision maker should always choose the treatment with the highest expected benefit, regardless of uncertainty. From this perspective, uncertainty can be seen as a source of "opportunity loss" owing to its associated chance of choosing the suboptimal treatment. Motivated from the concept of the expected value of perfect information (EVPI) in decision analysis, we quantify such opportunity loss and propose novel measures of uncertainty around the NNT: the Lost NNT and the Lost Opportunity Index (LOI). RESULTS: The Lost NNT is the quantification of the lost opportunity expressed on the same scale as the NNT. The LOI is a scale-free measure quantifying the loss in terms of the relative efficacy of treatment. We illustrate the method using a sample of published NNT values. CONCLUSION: Decision-theoretic concepts have the potential to be applied in this context to provide measures of uncertainty that can have relevant implications.