OBJECTIVE: We examined whether a breast cancer risk prediction model other than the Gail et al. model performs better at discriminating between women who will and who will not develop the disease. METHODS: We applied the two published versions of the Rosner and Colditz log-incidence model of breast cancer, developed on data from the Nurses' Health Study, to the estimation of 5-year risk for the period 1992 to 1997 in the same cohort. The first version contained reproductive factors only, and the second version contained a more extensive list of risk factors. RESULTS: Both versions of the model fit well. The ratio of expected to observed numbers of cases (E/O) in the first version was 1.00 (95% confidence interval [CI] 0.93-1.07); for the extended version the E/O was 1.01 (95% CI 0.94-1.09). The age-adjusted concordance statistic was 0.57 for the first model version and 0.63 for the extended version. CONCLUSION: The discriminatory accuracy of the two versions was modest, although the addition of the variables in the extended version meaningfully increased the discriminatory accuracy of risk prediction over that found with the more parsimonious model.
OBJECTIVE: We examined whether a breast cancer risk prediction model other than the Gail et al. model performs better at discriminating between women who will and who will not develop the disease. METHODS: We applied the two published versions of the Rosner and Colditz log-incidence model of breast cancer, developed on data from the Nurses' Health Study, to the estimation of 5-year risk for the period 1992 to 1997 in the same cohort. The first version contained reproductive factors only, and the second version contained a more extensive list of risk factors. RESULTS: Both versions of the model fit well. The ratio of expected to observed numbers of cases (E/O) in the first version was 1.00 (95% confidence interval [CI] 0.93-1.07); for the extended version the E/O was 1.01 (95% CI 0.94-1.09). The age-adjusted concordance statistic was 0.57 for the first model version and 0.63 for the extended version. CONCLUSION: The discriminatory accuracy of the two versions was modest, although the addition of the variables in the extended version meaningfully increased the discriminatory accuracy of risk prediction over that found with the more parsimonious model.
Authors: Hui Xu; Jing Qian; Nina P Paynter; Xuehong Zhang; Brian W Whitcomb; Shelley S Tworoger; Kathryn M Rexrode; Susan E Hankinson; Raji Balasubramanian Journal: Stat Med Date: 2018-11-22 Impact factor: 2.373
Authors: Shelley S Tworoger; Xuehong Zhang; A Heather Eliassen; Jing Qian; Graham A Colditz; Walter C Willett; Bernard A Rosner; Peter Kraft; Susan E Hankinson Journal: J Clin Oncol Date: 2014-08-18 Impact factor: 44.544
Authors: Mark Powell; Farid Jamshidian; Kate Cheyne; Joanne Nititham; Lee Ann Prebil; Rochelle Ereman Journal: Clin Breast Cancer Date: 2013-11-22 Impact factor: 3.225
Authors: A M D'Amelio; A Cassidy; K Asomaning; O Y Raji; S W Duffy; J K Field; M R Spitz; D Christiani; C J Etzel Journal: Br J Cancer Date: 2010-06-29 Impact factor: 7.640
Authors: Steven R Cummings; Jeffrey A Tice; Scott Bauer; Warren S Browner; Jack Cuzick; Elad Ziv; Victor Vogel; John Shepherd; Celine Vachon; Rebecca Smith-Bindman; Karla Kerlikowske Journal: J Natl Cancer Inst Date: 2009-03-10 Impact factor: 13.506