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Validating a model for predicting breast cancer and nonbreast cancer death in women aged 55 years and older.
Wolfson, Emily A; Schonberg, Mara A; Eliassen, A Heather; Bertrand, Kimberly A; Shvetsov, Yurii B; Rosner, Bernard A; Palmer, Julie R; LaCroix, Andrea Z; Chlebowski, Rowan T; Nelson, Rebecca A; Ngo, Long H.
Afiliación
  • Wolfson EA; Division of General Medicine and Primary Care, Department of Medicine, Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, MA, USA.
  • Schonberg MA; Division of General Medicine and Primary Care, Department of Medicine, Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, MA, USA.
  • Eliassen AH; Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA; Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Harvard School of Public Health, Boston, MA, USA.
  • Bertrand KA; Slone Epidemiology Center at Boston University and Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA.
  • Shvetsov YB; University of Hawaii Cancer Center, University of Hawaii at Manoa, Honolulu, HI, USA.
  • Rosner BA; Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA; Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Harvard School of Public Health, Boston, MA, USA.
  • Palmer JR; Slone Epidemiology Center at Boston University and Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA.
  • LaCroix AZ; Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, CA, USA.
  • Chlebowski RT; The Lundquist Institute, Torrance, CA, USA.
  • Nelson RA; Department of Computational and Quantitative Medicine, City of Hope, Duarte, CA, USA.
  • Ngo LH; Division of General Medicine and Primary Care, Department of Medicine, Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, MA, USA.
J Natl Cancer Inst ; 116(1): 81-96, 2024 01 10.
Article en En | MEDLINE | ID: mdl-37676833
BACKGROUND: To support mammography screening decision making, we developed a competing-risk model to estimate 5-year breast cancer risk and 10-year nonbreast cancer death for women aged 55 years and older using Nurses' Health Study data and examined model performance in the Black Women's Health Study (BWHS). Here, we examine model performance in predicting 10-year outcomes in the BWHS, Women's Health Initiative-Extension Study (WHI-ES), and Multiethnic Cohort (MEC) and compare model performance to existing breast cancer prediction models. METHODS: We used competing-risk regression and Royston and Altman methods for validating survival models to calculate our model's calibration and discrimination (C index) in BWHS (n = 17 380), WHI-ES (n = 106 894), and MEC (n = 49 668). The Nurses' Health Study development cohort (n = 48 102) regression coefficients were applied to the validation cohorts. We compared our model's performance with breast cancer risk assessment tool (Gail) and International Breast Cancer Intervention Study (IBIS) models by computing breast cancer risk estimates and C statistics. RESULTS: When predicting 10-year breast cancer risk, our model's C index was 0.569 in BWHS, 0.572 in WHI-ES, and 0.576 in MEC. The Gail model's C statistic was 0.554 in BWHS, 0.564 in WHI-ES, and 0.551 in MEC; IBIS's C statistic was 0.547 in BWHS, 0.552 in WHI-ES, and 0.562 in MEC. The Gail model underpredicted breast cancer risk in WHI-ES; IBIS underpredicted breast cancer risk in WHI-ES and in MEC but overpredicted breast cancer risk in BWHS. Our model calibrated well. Our model's C index for predicting 10-year nonbreast cancer death was 0.760 in WHI-ES and 0.763 in MEC. CONCLUSIONS: Our competing-risk model performs as well as existing breast cancer prediction models in diverse cohorts and predicts nonbreast cancer death. We are developing a website to disseminate our model.
Asunto(s)

Texto completo: 1 Base de datos: MEDLINE Asunto principal: Neoplasias de la Mama Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Female / Humans Idioma: En Revista: J Natl Cancer Inst Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos

Texto completo: 1 Base de datos: MEDLINE Asunto principal: Neoplasias de la Mama Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Female / Humans Idioma: En Revista: J Natl Cancer Inst Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos