© 2001 by Oxford University Press
Journal of the National Cancer Institute, Vol. 93, No. 5, 358-366,
March 7, 2001
© 2001 Oxford University Press
Validation of the Gail et al. Model of Breast Cancer Risk Prediction and Implications for Chemoprevention
Affiliations of authors: B. Rockhill, C. Byrne, Channing Laboratory, Harvard Medical School and Brigham and Women's Hospital, Boston, MA; D. Spiegelman, Departments of Epidemiology and Biostatistics, Harvard School of Public Health, Boston; D. J. Hunter, G. A. Colditz, Channing Laboratory, Harvard Medical School and Brigham and Women's Hospital, and Department of Epidemiology, Harvard School of Public Health.
Correspondence to: Beverly Rockhill, Ph.D., Channing Laboratory, 181 Longwood Ave., Boston, MA 02115 (e-mail: beverly.rockhill{at}channing.harvard.edu).
Background: Women and their clinicians are increasingly encouraged to use risk estimates derived from statistical models, primarily that of Gail et al., to aid decision making regarding potential prevention options for breast cancer, including chemoprevention with tamoxifen. Methods: We evaluated both the goodness of fit of the Gail et al. model 2 that predicts the risk of developing invasive breast cancer specifically and its discriminatory accuracy at the individual level in the Nurses' Health Study. We began with a cohort of 82 109 white women aged 45–71 years in 1992 and applied the model of Gail et al. to these women over a 5-year follow-up period to estimate a 5-year risk of invasive breast cancer. All statistical tests were two-sided. Results: The model fit well in the total sample (ratio of expected [E] to observed [O] numbers of cases = 0.94; 95% confidence interval [CI] = 0.89 to 0.99). Underprediction was slightly greater for younger women (<60 years), but in most age and risk factor strata, E/O ratios were close to 1.0. The model fit equally well (E/O ratio = 0.93; 95% CI = 0.87 to 0.99) in a subset of women reporting recent screening (i.e., within 1 year before the baseline); among women with an estimated 5-year risk of developing invasive breast cancer of 1.67% or greater, the E/O ratio was 1.04 (95% CI = 0.96 to 1.12). The concordance statistic, which indicates discriminatory accuracy, for the Gail et al. model 2 when used to estimate 5-year risk was 0.58 (95% CI = 0.56 to 0.60). Only 3.3% of the 1354 cases of breast cancer observed in the cohort arose among women who fell into age–risk strata expected to have statistically significant net health benefits from prophylactic tamoxifen use. Conclusions: The Gail et al. model 2 fit well in this sample in terms of predicting numbers of breast cancer cases in specific risk factor strata but had modest discriminatory accuracy at the individual level. This finding has implications for use of the model in clinical counseling of individual women.
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