Mara A Schonberg1,2, Vicky W Li3, A Heather Eliassen4,5, Roger B Davis3, Andrea Z LaCroix6, Ellen P McCarthy3, Bernard A Rosner4,5, Rowan T Chlebowski7, Susan E Hankinson4,5,8, Edward R Marcantonio3, Long H Ngo3. 1. Division of General Medicine and Primary Care, Department of Medicine, Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, MA, USA. mschonbe@bidmc.harvard.edu. 2. Beth Israel Deaconess Medical Center, 1309 Beacon, Office 219, Brookline, MA, 02446, USA. mschonbe@bidmc.harvard.edu. 3. Division of General Medicine and Primary Care, Department of Medicine, Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, MA, USA. 4. Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA. 5. Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA. 6. Division of Epidemiology, Family Medicine and Public Health, University of California San Diego, La Jolla, CA, USA. 7. Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Torrance, CA, USA. 8. Department of Biostatistics and Epidemiology, University of Massachusetts, 713 North Pleasant Street, Amherst, MA, USA.
Abstract
PURPOSE: Accurate risk assessment is necessary for decision-making around breast cancer prevention. We aimed to develop a breast cancer prediction model for postmenopausal women that would take into account their individualized competing risk of non-breast cancer death. METHODS: We included 73,066 women who completed the 2004 Nurses' Health Study (NHS) questionnaire (all ≥57 years) and followed participants until May 2014. We considered 17 breast cancer risk factors (health behaviors, demographics, family history, reproductive factors) and 7 risk factors for non-breast cancer death (comorbidities, functional dependency) and mammography use. We used competing risk regression to identify factors independently associated with breast cancer. We validated the final model by examining calibration (expected-to-observed ratio of breast cancer incidence, E/O) and discrimination (c-statistic) using 74,887 subjects from the Women's Health Initiative Extension Study (WHI-ES; all were ≥55 years and followed for 5 years). RESULTS: Within 5 years, 1.8 % of NHS participants were diagnosed with breast cancer (vs. 2.0 % in WHI-ES, p = 0.02), and 6.6 % experienced non-breast cancer death (vs. 5.2 % in WHI-ES, p < 0.001). Using a model selection procedure which incorporated the Akaike Information Criterion, c-statistic, statistical significance, and clinical judgement, our final model included 9 breast cancer risk factors, 5 comorbidities, functional dependency, and mammography use. The model's c-statistic was 0.61 (95 % CI [0.60-0.63]) in NHS and 0.57 (0.55-0.58) in WHI-ES. On average, our model under predicted breast cancer in WHI-ES (E/O 0.92 [0.88-0.97]). CONCLUSIONS: We developed a novel prediction model that factors in postmenopausal women's individualized competing risks of non-breast cancer death when estimating breast cancer risk.
PURPOSE: Accurate risk assessment is necessary for decision-making around breast cancer prevention. We aimed to develop a breast cancer prediction model for postmenopausal women that would take into account their individualized competing risk of non-breast cancer death. METHODS: We included 73,066 women who completed the 2004 Nurses' Health Study (NHS) questionnaire (all ≥57 years) and followed participants until May 2014. We considered 17 breast cancer risk factors (health behaviors, demographics, family history, reproductive factors) and 7 risk factors for non-breast cancer death (comorbidities, functional dependency) and mammography use. We used competing risk regression to identify factors independently associated with breast cancer. We validated the final model by examining calibration (expected-to-observed ratio of breast cancer incidence, E/O) and discrimination (c-statistic) using 74,887 subjects from the Women's Health Initiative Extension Study (WHI-ES; all were ≥55 years and followed for 5 years). RESULTS: Within 5 years, 1.8 % of NHSparticipants were diagnosed with breast cancer (vs. 2.0 % in WHI-ES, p = 0.02), and 6.6 % experienced non-breast cancer death (vs. 5.2 % in WHI-ES, p < 0.001). Using a model selection procedure which incorporated the Akaike Information Criterion, c-statistic, statistical significance, and clinical judgement, our final model included 9 breast cancer risk factors, 5 comorbidities, functional dependency, and mammography use. The model's c-statistic was 0.61 (95 % CI [0.60-0.63]) in NHS and 0.57 (0.55-0.58) in WHI-ES. On average, our model under predicted breast cancer in WHI-ES (E/O 0.92 [0.88-0.97]). CONCLUSIONS: We developed a novel prediction model that factors in postmenopausal women's individualized competing risks of non-breast cancer death when estimating breast cancer risk.
Entities:
Keywords:
Breast cancer prediction; Competing risks; Older
Authors: Garnet L Anderson; Rowan T Chlebowski; Aaron K Aragaki; Lewis H Kuller; JoAnn E Manson; Margery Gass; Elizabeth Bluhm; Stephanie Connelly; F Allan Hubbell; Dorothy Lane; Lisa Martin; Judith Ockene; Thomas Rohan; Robert Schenken; Jean Wactawski-Wende Journal: Lancet Oncol Date: 2012-03-07 Impact factor: 41.316
Authors: Heather K Neilson; Christine M Friedenreich; Nigel T Brockton; Robert C Millikan Journal: Cancer Epidemiol Biomarkers Prev Date: 2009-01 Impact factor: 4.254
Authors: Sara J Schonfeld; David Pee; Robert T Greenlee; Patricia Hartge; James V Lacey; Yikyung Park; Arthur Schatzkin; Kala Visvanathan; Ruth M Pfeiffer Journal: J Clin Oncol Date: 2010-04-05 Impact factor: 44.544
Authors: Z Huang; S E Hankinson; G A Colditz; M J Stampfer; D J Hunter; J E Manson; C H Hennekens; B Rosner; F E Speizer; W C Willett Journal: JAMA Date: 1997-11-05 Impact factor: 56.272
Authors: Carol Sweeney; Cindy K Blair; Kristin E Anderson; DeAnn Lazovich; Aaron R Folsom Journal: Am J Epidemiol Date: 2004-11-01 Impact factor: 4.897
Authors: Jeffrey A Tice; Steven R Cummings; Rebecca Smith-Bindman; Laura Ichikawa; William E Barlow; Karla Kerlikowske Journal: Ann Intern Med Date: 2008-03-04 Impact factor: 25.391
Authors: Mara A Schonberg; Vicky W Li; A Heather Eliassen; Roger B Davis; Andrea Z LaCroix; Ellen P McCarthy; Bernard A Rosner; Rowan T Chlebowski; Thomas E Rohan; Susan E Hankinson; Edward R Marcantonio; Long H Ngo Journal: J Natl Cancer Inst Date: 2015-11-30 Impact factor: 11.816
Authors: Mara A Schonberg; Christine E Kistler; Adlin Pinheiro; Alicia R Jacobson; Gianna M Aliberti; Maria Karamourtopoulos; Michelle Hayes; Bridget A Neville; Carmen L Lewis; Christina C Wee; Angela Fagerlin; Larissa Nekhlyudov; Edward R Marcantonio; Mary Beth Hamel; Roger B Davis Journal: JAMA Intern Med Date: 2020-06-01 Impact factor: 21.873