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Accounting for individualized competing mortality risks in estimating postmenopausal breast cancer risk

  • Epidemiology
  • Published:
Breast Cancer Research and Treatment Aims and scope Submit manuscript

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.

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Abbreviations

AIC:

Akaike information criterion

BCRAT:

Breast cancer risk assessment tool

BCSC:

Breast cancer surveillance consortium model

CI:

Confidence interval

CRR:

Competing risk regression

E/O:

Expected-to-observed ratio

MI:

Myocardial infarction

NHS:

Nurses’ health study

PHR:

Proportional hazards regression

SEER:

Surveillance epidemiology and end results

WHI:

Women’s health initiative study

WHI-CT:

Women’s health initiative clinical trials

WHI-ES:

Women’s health initiative extension study

WHI-OS:

Women’s health initiative observational study

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Acknowledgments

We would like to thank the participants and staff of the Nurses’ Health Study for their valuable contributions as well as the following state cancer registries for their help: AL, AZ, AR, CA, CO, CT, DE, FL, GA, ID, IL, IN, IA, KY, LA, ME, MD, MA, MI, NE, NH, NJ, NY, NC, ND, OH, OK, OR, PA, RI, SC, TN, TX, VA, WA, and WY. In addition, this study was approved by the Connecticut Department of Public Health (DPH) Human Investigations Committee. Certain data used in this publication were obtained from the DPH. The WHI program is funded by the National Heart, Lung, and Blood Institute, National Institutes of Health, the U.S. Department of Health and Human Services through contracts HHSN268201100046C, HHSN268201100001C, HHSN268201100002C, HHSN268201100003C, HHSN268201100004C, and HHSN271201100004C. We would also like to thank the following WHI INVESTIGATORS for their help with this project: Program Office: (National Heart, Lung, and Blood Institute, Bethesda, Maryland) Jacques Rossouw, Shari Ludlam, Dale Burwen, Joan McGowan, Leslie Ford, and Nancy Geller Clinical Coordinating Center: Clinical Coordinating Center: (Fred Hutchinson Cancer Research Center, Seattle, WA) Garnet Anderson, Ross Prentice, Andrea LaCroix, and Charles Kooperberg Investigators and Academic Centers: (Brigham and Women’s Hospital, Harvard Medical School, Boston, MA) JoAnn E. Manson; (MedStar Health Research Institute/Howard University, Washington, DC) Barbara V. Howard; (Stanford Prevention Research Center, Stanford, CA) Marcia L. Stefanick; (The Ohio State University, Columbus, OH) Rebecca Jackson; (University of Arizona, Tucson/Phoenix, AZ) Cynthia A. Thomson; (University at Buffalo, Buffalo, NY) Jean Wactawski-Wende; (University of Florida, Gainesville/Jacksonville, FL) Marian Limacher; (University of Iowa, Iowa City/Davenport, IA) Robert Wallace; (University of Pittsburgh, Pittsburgh, PA) Lewis Kuller; (Wake Forest University School of Medicine, Winston-Salem, NC) Sally Shumaker We would also like to thank Jonathan Yee for his help with data entry for this manuscript. NOTES: The sponsor had no role in the design of the study, the collection, analysis, and interpretation of the data, the writing of the manuscript, or the decision to submit the manuscript for publication. This work was presented in part at the 9th annual meeting of the Cancer and Primary Care Research International Network April 28th, 2016 in Boston, MA.

Funding

This work was supported by the National Institute on Aging at the National Institutes of Health (R01 AG041860), an NHS cohort infrastructure Grant (UM1 CA186107), and an NHS program project Grant (P01 CA87969). The authors declare that they have no conflict of interest.

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Authors and Affiliations

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

    Mara A. Schonberg, Vicky W. Li, Roger B. Davis, Ellen P. McCarthy, Edward R. Marcantonio & Long H. Ngo

  2. Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA

    A. Heather Eliassen, Bernard A. Rosner & Susan E. Hankinson

  3. Channing Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

    A. Heather Eliassen, Bernard A. Rosner & Susan E. Hankinson

  4. Division of Epidemiology, Family Medicine and Public Health, University of California San Diego, La Jolla, CA, USA

    Andrea Z. LaCroix

  5. Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Torrance, CA, USA

    Rowan T. Chlebowski

  6. Department of Biostatistics and Epidemiology, University of Massachusetts, 713 North Pleasant Street, Amherst, MA, USA

    Susan E. Hankinson

  7. Beth Israel Deaconess Medical Center, 1309 Beacon, Office 219, Brookline, MA, 02446, USA

    Mara A. Schonberg

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  1. Mara A. Schonberg
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  2. Vicky W. Li
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  3. A. Heather Eliassen
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  4. Roger B. Davis
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  6. Ellen P. McCarthy
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  7. Bernard A. Rosner
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  8. Rowan T. Chlebowski
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  9. Susan E. Hankinson
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  10. Edward R. Marcantonio
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  11. Long H. Ngo
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Corresponding author

Correspondence to Mara A. Schonberg.

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Schonberg, M.A., Li, V.W., Eliassen, A.H. et al. Accounting for individualized competing mortality risks in estimating postmenopausal breast cancer risk. Breast Cancer Res Treat 160, 547–562 (2016). https://doi.org/10.1007/s10549-016-4020-8

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  • DOI: https://doi.org/10.1007/s10549-016-4020-8

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