Deborah A Boggs1, Lynn Rosenberg1, Lucile L Adams-Campbell1, Julie R Palmer2. 1. Deborah A. Boggs, Lynn Rosenberg, and Julie R. Palmer, Slone Epidemiology Center at Boston University, Boston, MA; and Lucile L. Adams-Campbell, Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC. 2. Deborah A. Boggs, Lynn Rosenberg, and Julie R. Palmer, Slone Epidemiology Center at Boston University, Boston, MA; and Lucile L. Adams-Campbell, Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC. jpalmer@bu.edu.
Abstract
PURPOSE: Breast cancer risk prediction models have underestimated risk for African American women, contributing to lower recruitment rates in prevention trials. A model previously developed for African American women was found to underestimate risk in the Black Women's Health Study (BWHS). METHODS: We developed a breast cancer risk model for African American women using relative risks derived from 10 years of follow-up of BWHS participants age 30 to 69 years at baseline. Using the subsequent 5 years of follow-up data, we evaluated calibration as the ratio of expected to observed number of breast cancers and assessed discriminatory accuracy using the concordance statistic. RESULTS: The BWHS model included family history, previous biopsy, body mass index at age 18 years, age at menarche, age at first birth, oral contraceptive use, bilateral oophorectomy, estrogen plus progestin use, and height. There was good agreement between predicted and observed number of breast cancers overall (expected-to-observed ratio, 0.96; 95% CI, 0.88 to 1.05) and in most risk factor categories. Discriminatory accuracy was higher for women younger than age 50 years (area under the curve [AUC], 0.62; 95% CI, 0.58 to 0.65) than for women age ≥ 50 years (AUC, 0.56; 95% CI, 0.53 to 0.59). Using a 5-year predicted risk of 1.66% or greater as a cut point, 2.8% of women younger than 50 years old and 32.2% of women ≥ 50 years old were classified as being at elevated risk of invasive breast cancer. CONCLUSION: The BWHS model was well calibrated overall, and the predictive ability was best for younger women. The proportion of women predicted to meet the 1.66% cut point commonly used to determine eligibility for breast cancer prevention trials was greatly increased relative to previous models.
PURPOSE:Breast cancer risk prediction models have underestimated risk for African American women, contributing to lower recruitment rates in prevention trials. A model previously developed for African American women was found to underestimate risk in the Black Women's Health Study (BWHS). METHODS: We developed a breast cancer risk model for African American women using relative risks derived from 10 years of follow-up of BWHS participants age 30 to 69 years at baseline. Using the subsequent 5 years of follow-up data, we evaluated calibration as the ratio of expected to observed number of breast cancers and assessed discriminatory accuracy using the concordance statistic. RESULTS: The BWHS model included family history, previous biopsy, body mass index at age 18 years, age at menarche, age at first birth, oral contraceptive use, bilateral oophorectomy, estrogen plus progestin use, and height. There was good agreement between predicted and observed number of breast cancers overall (expected-to-observed ratio, 0.96; 95% CI, 0.88 to 1.05) and in most risk factor categories. Discriminatory accuracy was higher for women younger than age 50 years (area under the curve [AUC], 0.62; 95% CI, 0.58 to 0.65) than for women age ≥ 50 years (AUC, 0.56; 95% CI, 0.53 to 0.59). Using a 5-year predicted risk of 1.66% or greater as a cut point, 2.8% of women younger than 50 years old and 32.2% of women ≥ 50 years old were classified as being at elevated risk of invasive breast cancer. CONCLUSION: The BWHS model was well calibrated overall, and the predictive ability was best for younger women. The proportion of women predicted to meet the 1.66% cut point commonly used to determine eligibility for breast cancer prevention trials was greatly increased relative to previous models.
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