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35Good health: Statistical challenges inpersonalizing disease preventionAlice S. WhittemoreDepartment of Health Research and PolicyStanford University, Stanford, CAIncreasingly, patients and clinicians are basing healthcare decisions on statisticalmodels that use a person’s covariates to assign him/her a probability ofdeveloping a disease in a given future time period. In this chapter, I describesome of the statistical problems that arise when evaluating the accuracy andutility of these models.35.1 IntroductionRising health care costs underscore the need for cost-effective disease preventionand control. To achieve cost-efficiency, preventive strategies must focus onindividuals whose genetic and lifestyle characteristics put them at highest risk.To identify these individuals, statisticians and public health professionals aredeveloping personalized risk models for many diseases and other adverse outcomes.The task of checking the accuracy and utility of these models requiresnew statistical methods and new applications for existing methods.35.2 How do we personalize disease risks?We do this using a personalized risk model, which is an algorithm that assignsa person a probability of developing an adverse outcome in a given future timeperiod (say, five, ten or twenty years). The algorithm combines his/her valuesfor a set of risk-associated covariates with regional incidence and mortalitydata and quantitative evidence of the covariates’ effects on risk of the outcome.391