Quantifying the relationship between age at diagnosis and breast cancer-specific mortality

  • Helen M. Johnson
  • William Irish
  • Mahvish Muzaffar
  • Nasreen A. Vohra
  • Jan H. WongEmail author



The relationship between age at diagnosis and breast cancer-specific mortality (BCSM) is unclear. The aim of this study was to examine the nature of this relationship using rigorous statistical methodology.


A historical cohort study of adult women with invasive breast cancer in the SEER database from 2000 to 2015 was conducted. Multivariable Cox’s cause-specific hazards model was used to evaluate the association of age at diagnosis with risk of BCSM. Functional relationship of age was assessed using cumulative sums of Martingale residuals and the Kolmogorov-type supremum test.


A total of 206,332 women were eligible for study. Mean age at diagnosis was 59.7 ± 13.8 years. Median follow-up was 80 months. During the study period, 21,771 women (10.6%) died from breast cancer and 18,566 (9.0%) died from other causes. Cumulative incidence of BCSM at 120 months post-diagnosis was 14.4% (95% CI 14.2–14.6%). Age was found to be quadratically related to the risk of BCSM (p < 0.001), with a nadir at 45 years of age. The final Cox model suggests that a 30-year-old woman has approximately the same adjusted BCSM risk (HR 1.187, 95% CI 1.187–1.188) as a 60-year-old woman (HR 1.174, 95% CI 1.174–1.175).


Women diagnosed with breast cancer at the extremes of age suffer disproportionate rates of cancer-specific mortality. The relationship between age at diagnosis and adjusted risk of BCSM is complex, consistent with a quadratic function. With the growing appreciation for breast cancer as a heterogeneous disease, it is essential to accurately address age as a prognostic risk factor in predictive models.


Breast cancer Mortality Differential mortality Age groups Age distribution Statistical model 



Akaike information criterion


American Joint Committee on Cancer


Breast cancer-specific mortality


Confidence interval


Standard deviation


Estrogen receptor


Human epidermal growth factor receptor


Hazard ratio


Progesterone receptor


Surveillance, epidemiology, and end results


Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed consent

As this study is based on a publicly available database without identifying patient information, informed consent was not needed.

Supplementary material

10549_2019_5353_MOESM1_ESM.tiff (199.5 mb)
Supplementary material 1 (TIFF 204308 kb). Supplemental Fig. 1. Subgroup analysis (2010–2015). Observed cumulative Martingale residual plot against age at diagnosis with 20 simulated realizations. This plot is used to determine the functional form of age on the risk of breast cancer-specific mortality
10549_2019_5353_MOESM2_ESM.tiff (92.2 mb)
Supplementary material 2 (TIFF 94405 kb). Supplemental Fig. 2. Subgroup analysis (2010–2015). Akaike information criterion (AIC) for candidate Cox models. The model with the smallest value is considered the best model
10549_2019_5353_MOESM3_ESM.tiff (137.3 mb)
Supplementary material 3 (TIFF 140634 kb). Supplemental Fig. 3. Subgroup analysis (2010–2015). The plot of the hazard ratio for increasing age based on the fit of the Cox model with age at diagnosis included as a quadratic term. The superimposed histogram demonstrates that the age distribution of is approximately normal
10549_2019_5353_MOESM4_ESM.tiff (91.7 mb)
Supplementary material 4 (TIFF 93898 kb). Supplemental Fig. 4. Subgroup analysis (2010–2015). Risk of breast cancer-specific mortality over time with age included as a time-dependent covariate


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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Helen M. Johnson
    • 1
  • William Irish
    • 1
  • Mahvish Muzaffar
    • 2
  • Nasreen A. Vohra
    • 1
  • Jan H. Wong
    • 1
    Email author
  1. 1.Division of Surgical Oncology, Department of SurgeryEast Carolina University Brody School of MedicineGreenvilleUSA
  2. 2.Department of Medicine, Division of Hematology/OncologyEast Carolina University Brody School of MedicineGreenvilleUSA

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