A Multi-State Survival Model for Time to Breast Cancer Mortality among a Cohort of Initially Disease-Free Women

Author:

Rosner Bernard12ORCID,Glynn Robert J.12ORCID,Eliassen A. Heather134ORCID,Hankinson Susan E.145ORCID,Tamimi Rulla M.146ORCID,Chen Wendy Y.17ORCID,Holmes Michelle D.14ORCID,Mu Yi1ORCID,Peng Cheng1ORCID,Colditz Graham A.18ORCID,Willett Walter C.34ORCID,Tworoger Shelley S.149ORCID

Affiliation:

1. 1Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts.

2. 2Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts.

3. 3Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, Massachusetts.

4. 4Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts.

5. 5Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, Massachusetts.

6. 6Department of Population Health Sciences, Weill Cornell Medicine, New York, New York.

7. 7Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts.

8. 8Siteman Cancer Center and Washington University School of Medicine, Saint Louis, Missouri.

9. 9Department of Cancer Epidemiology, Moffitt Cancer Center, Tampa, Florida.

Abstract

Abstract Background: Identifying risk factors for aggressive forms of breast cancer is important. Tumor factors (e.g., stage) are important predictors of prognosis, but may be intermediates between prediagnosis risk factors and mortality. Typically, separate models are fit for incidence and mortality postdiagnosis. These models have not been previously integrated to identify risk factors for lethal breast cancer in cancer-free women. Methods: We combined models for breast cancer incidence and breast cancer–specific mortality among cases into a multi-state survival model for lethal breast cancer. We derived the model from cancer-free postmenopausal Nurses’ Health Study women in 1990 using baseline risk factors. A total of 4,391 invasive breast cancer cases were diagnosed from 1990 to 2014 of which 549 died because of breast cancer over the same period. Results: Some established risk factors (e.g., family history, estrogen plus progestin therapy) were not associated with lethal breast cancer. Controlling for age, the strongest risk factors for lethal breast cancer were weight gain since age 18: > 30 kg versus ± 5 kg, RR = 1.94 [95% confidence interval (CI) = 1.38–2.74], nulliparity versus age at first birth (AAFB) < 25, RR = 1.60 (95% CI = 1.16–2.22), and current smoking ≥ 15 cigarettes/day versus never, RR = 1.42 (95% CI = 1.07–1.89). Conclusions: Some breast cancer incidence risk factors are not associated with lethal breast cancer; other risk factors for lethal breast cancer are not associated with disease incidence. Impact: This multi-state survival model may be useful for identifying prediagnosis factors that lead to more aggressive and ultimately lethal breast cancer.

Funder

NCI

Publisher

American Association for Cancer Research (AACR)

Subject

Oncology,Epidemiology

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