Validating a model for predicting breast cancer and nonbreast cancer death in women aged 55 years and older

Author:

Wolfson Emily A1,Schonberg Mara A1ORCID,Eliassen A Heather2ORCID,Bertrand Kimberly A3ORCID,Shvetsov Yurii B4ORCID,Rosner Bernard A2ORCID,Palmer Julie R3ORCID,LaCroix Andrea Z5ORCID,Chlebowski Rowan T6ORCID,Nelson Rebecca A7ORCID,Ngo Long H18ORCID

Affiliation:

1. Division of General Medicine and Primary Care, Department of Medicine, Harvard Medical School, Beth Israel Deaconess Medical Center , Boston, MA, USA

2. 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

3. Slone Epidemiology Center at Boston University and Department of Medicine, Boston University Chobanian & Avedisian School of Medicine , Boston, MA, USA

4. University of Hawaii Cancer Center, University of Hawaii at Manoa , Honolulu, HI, USA

5. Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego , La Jolla, CA, USA

6. The Lundquist Institute , Torrance, CA, USA

7. Department of Computational and Quantitative Medicine, City of Hope , Duarte, CA, USA

8. Department of Biostatistics, Harvard T.H. Chan School of Public Health , Boston, MA, USA

Abstract

Abstract 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.

Funder

National Cancer Institute

National Institutes of Health

NHS

Karin Grunebaum Cancer Research Foundation

Susan G. Komen Foundation

Publisher

Oxford University Press (OUP)

Subject

Cancer Research,Oncology

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3