Editorial Personalizing Evidence-Based Primary Prevention With Aspirin Individualized Risks and Patient Preference

One of the unfortunate side consequences of evidence-based medicine (EBM) is that sometimes there is no escaping how small the benefit of any individual treatment may seem. In the “days of the giants, ” doctors saved lives; now we can consult league tables that bloodlessly inform us how many patients—10, 50, or 200—must receive a treat-ment to avert even a single bad outcome,1 EBM’s venerable number needed to treat. Article see p 268 The number needed to treat is, of course, based only on average treatment effects. A limitation of EBM is that we study groups of patients, whereas we treat individuals. This mismatch can be partially addressed by the use of statistical models to calculate the important risks that determine a more “personalized ” probability of benefit. When this is done, it turns out, the degree of benefit for an intervention in an