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A.S. Whittemore 401Consider, for example, the simple problem of combining existing models forbreast cancer and stroke to assign to each cohort subject a vector of theform (P (B),P(S),P(D), P (BS)), where, for example, P (B) isherassignedrisk of developing breast cancer, and P (BS) isherassignedriskofdevelopingboth breast cancer and stroke, during the risk period. The resultingmultistate model would allow the possibility of within-person correlation inoutcome-specific risks.35.6 Concluding remarksThis chapter has outlined some statistical problems that arise when assigningpeople individualized probabilities of adverse health outcomes, and when evaluatingthe utility of these assignments. Like other areas in statistics, this workrests on foundations that raise philosophical issues. For example, the discussionof risk model calibration assumes that each individual has an unknownprobability of developing the outcome of interest in the given time period. Thispresumed probability depends on his/her values for a set of risk factors, onlysome of which are known. But, unlike a parameter that governs a statisticaldistribution, one person’s “true risk” does not lend itself to straightforwarddefinition. Yet, much of the previous discussion requires this assumption.Even when the assumption is accepted, fundamental issues arise. How canwe estimate one woman’s breast cancer probability without aggregating hersurvival data with those of others who may have different risks? And howmuch of a woman’s breast cancer risk is due purely to chance? If we knew thecombined effects of all measurable breast cancer risk factors, and if we couldapply this knowledge to assign risks to disease-free women, how much residualvariation in subsequent outcomes might we see?These issues notwithstanding, it seems clear that the need for cost-efficient,high quality health care will mandate individualized strategies for preventionand treatment. Difficult cost-benefit tradeoffs will become increasingly commonas we discover new drugs and therapies with adverse side effects. Patientsand their clinical caregivers need rigorous, evidence-based guidance in makingthe choices confronting them.ReferencesAkritas, M.G. (1994). Nearest neighbor estimation of a bivariate distributionunder random censoring. The Annals of Statistics, 22:1299–1327.