The Numbers Game: The Risky Business of Projecting Risk

Abstract

JNCI Vol. 103, Issue 13 | July 6, 2011 In this issue of the Journal, Petracci et al. ( 1 ) present a model to estimate the degree of benefit in terms of breast cancer risk reduction that may be achieved with risk factor modification. By how much would breast cancer risk change over a 10-year period for a 45-yearold Italian woman making changes in modifiable risk factors such as exercise, weight , and/or alcohol use? According to this well-designed and conducted study, all of the following answers are correct: 0.4% , 0.6%, and 20.5%. These figures refer to individual absolute risk reduction, population-level absolute risk reduction, and population fractional risk reduction (akin to the population attributable risk), respectively, and are associated with change in one (individual absolute risk reduction) or all modifiable risk factors (population-level risk reduction estimates). The compatible, yet disparate numbers, for the absolute risk reduction and fractional risk reduction risk estimates is a difficult concept to grasp — even more challenging is when and how to apply the estimates. The results of this study provide a welcome spotlight on the complexity of predicting, interpreting, and communicating risk on both an individual and population level. By sound statistical methodology and data from a welldesigned , large , Italian , multicenter case – control study of invasive breast cancer, Petracci et al. ( 1 ) extend previous work on breast cancer prediction models by incorporating modifi able risk factors and assessing the potential reductions in risk that might result if the modifi able risk factor profi le could be optimized. The calibration and discriminatory ability of the model when evaluated in an independent cohort was similar to other prediction models ( 1 , 2 ). The Numbers Game: The Risky Business of Projecting Risk