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Using multiple risk models with preventive interventions


  • Mitchell H. Gail

    Corresponding author
    • Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, U.S.A
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  • Supporting information may be found in the online version of this article.

Mitchell H. Gail, National Cancer Institute, EPS-8032, Rockville, MD, 20892-7244, U.S.A.



An ideal preventive intervention would have negligible side effects and could be applied to the entire population, thus achieving maximal preventive impact. Unfortunately, many interventions have adverse effects and beneficial effects. For example, tamoxifen reduces the risk of breast cancer by about 50% and the risk of hip fracture by 45%, but increases the risk of stroke by about 60%; other serious adverse effects include endometrial cancer and pulmonary embolus. Hence, tamoxifen should only be given to the subset of the population with high enough risks of breast cancer and hip fracture such that the preventive benefits outweigh the risks. Recommendations for preventive use of tamoxifen have been based primarily on breast cancer risk. Age-specific and race-specific rates were considered for other health outcomes, but not risk models. In this paper, we investigate the extent to which modeling not only the risk of breast cancer, but also the risk of stroke, can improve the decision to take tamoxifen. These calculations also give insight into the relative benefits of improving the discriminatory accuracy of such risk models versus improving the preventive effectiveness or reducing the adverse risks of the intervention. Depending on the discriminatory accuracies of the risk models, there may be considerable advantage to modeling the risks of more than one health outcome. Published 2012. This article is a US Government work and is in the public domain in the USA.