title: Modeling the effect of age in T1-2 breast cancer using the SEER database.
abstract: BACKGROUND
  Modeling the relationship between age and mortality for breast cancer patients may have important prognostic and therapeutic implications.
  
  
  METHODS
  Data from 9 registries of the Surveillance, Epidemiology, and End Results Program (SEER) of the United States were used. This study employed proportional hazards to model mortality in women with T1-2 breast cancers. The residuals of the model were used to examine the effect of age on mortality. This procedure was applied to node-negative (N0) and node-positive (N+) patients. All causes mortality and breast cancer specific mortality were evaluated.
  
  
  RESULTS
  The relationship between age and mortality is biphasic. For both N0 and N+ patients among the T1-2 group, the analysis suggested two age components. One component is linear and corresponds to a natural increase of mortality with each year of age. The other component is quasi-quadratic and is centered around age 50. This component contributes to an increased risk of mortality as age increases beyond 50. It suggests a hormonally related process: the farther from menopause in either direction, the more prognosis is adversely influenced by the quasi-quadratic component. There is a complex relationship between hormone receptor status and other prognostic factors, like age.
  
  
  CONCLUSIONS
  The present analysis confirms the findings of many epidemiological and clinical trials that the relationship between age and mortality is biphasic. Compared with older patients, young women experience an abnormally high risk of death. Among elderly patients, the risk of death from breast cancer does not decrease with increasing age. These facts are important in the discussion of options for adjuvant treatment with breast cancer patients.
authors.affiliation: Department of Radiation Oncology, University of Saskatchewan, Faculty of Medicine, Regina, Canada. patricia.tai@scf.sk.ca
authors.forename: Patricia
authors.initials: P
authors.lastname: Tai
authors.forename: Gábor
authors.initials: G
authors.lastname: Cserni
authors.forename: Jan
authors.initials: J
authors.lastname: Van De Steene
authors.forename: Georges
authors.initials: G
authors.lastname: Vlastos
authors.forename: Mia
authors.initials: M
authors.lastname: Voordeckers
authors.forename: Melanie
authors.initials: M
authors.lastname: Royce
authors.forename: Sang-Joon
authors.initials: SJ
authors.lastname: Lee
authors.forename: Vincent
authors.initials: V
authors.lastname: Vinh-Hung
authors.forename: Guy
authors.initials: G
authors.lastname: Storme
mesh_terms: D000328:Adult; D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D001943:Breast Neoplasms / Q000175:diagnosis* / Q000453:epidemiology* / Q000473:pathology; D018572:Disease-Free Survival; D005260:Female; D006801:Humans; D008875:Middle Aged; D015233:Models, Statistical; D008962:Models, Theoretical; D011379:Prognosis; D016016:Proportional Hazards Models; D011980:Receptors, Progesterone / Q000378:metabolism; D018426:SEER Program
chemical_list: D011980:Receptors, Progesterone