title: Real-world impact of non-breast cancer-specific death on overall survival in resectable breast cancer.
abstract: BACKGROUND
  The real-world occurrence rate of non-breast cancer-specific death (non-BCSD) and its impact on patients with breast cancer are poorly recognized.
  
  
  METHODS
  Women with resectable breast cancer from 1990 to 2007 in the Surveillance, Epidemiology, and End Results database (n = 199,963) were analyzed. The outcome events of breast cancer were classified as breast cancer-specific death (BCSD), non-BCSD, or survival. Binary logistics was used to estimate the occurrence rates of non-BCSD and BCSD with different clinicopathological factors. The Gray method was used to measure the cumulative incidence of non-BCSD and BCSD. The ratio of non-BCSDs to all causes of death and stacked cumulative incidence function plots were used to present the impact of non-BCSD on overall survival (OS). Models of Cox proportional hazards regression and competing risk regression were compared to highlight the suitable model.
  
  
  RESULTS
  There were 12,879 non-BCSDs (6.44%) and 28,784 BCSDs (14.39%). The oldest age group (>62 years), black race, and a single or divorced marital status were associated with more non-BCSDs. With adjustments for age, a hormone receptor-positive (HoR+) status was no longer related to increased non-BCSDs. In patients with grade 1, stage I disease and an HoR+ status as well as the oldest subgroup, a great dilution of non-BCSD on all causes of death could be observed, and this led to incorrect interpretations. The inaccuracy, caused by the commonly used Cox proportional hazards model, could be corrected by a competing risk model.
  
  
  CONCLUSIONS
  OS was largely impaired by non-BCSD during early breast cancer. For some future clinical trial planning, especially for the oldest patients and those with HoR+ breast cancer, non-BCSD should be considered a competing risk event. Cancer 2017;123:2432-43. © 2017 American Cancer Society.
authors.affiliation: Department of Oncology, Zhejiang University Jinhua Hospital, Jinhua, China.
authors.forename: Jianfei
authors.initials: J
authors.lastname: Fu
authors.affiliation: Department of Gastroenterology, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China.|Institute of Gastroenterology, Zhejiang University, Hangzhou, China.
authors.forename: Lunpo
authors.initials: L
authors.lastname: Wu
authors.affiliation: Department of Radiation Oncology, First Affiliated Hospital of Zhejiang Chinese Medical University, Zhejiang Provincial Hospital of Traditional Chinese Medicine, Hangzhou, China.
authors.forename: Mengjie
authors.initials: M
authors.lastname: Jiang
authors.affiliation: Department of Medical Oncology, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China.|Cancer Institute (Key Laboratory of Cancer Prevention and Intervention, Chinese National Ministry of Education; Key Laboratory of Molecular Biology in Medical Sciences, Zhejiang Province, China), Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China.
authors.forename: Dan
authors.initials: D
authors.lastname: Li
authors.affiliation: Department of Nuclear Medicine, Zhejiang University Jinhua Hospital, Jinhua, China.
authors.forename: Ting
authors.initials: T
authors.lastname: Jiang
authors.affiliation: Johns Hopkins University School of Medicine, Baltimore, Maryland.
authors.forename: Wei
authors.initials: W
authors.lastname: Fu
authors.affiliation: Department of Gastroenterology, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China.|Institute of Gastroenterology, Zhejiang University, Hangzhou, China.
authors.forename: Liangjing
authors.initials: L
authors.lastname: Wang
authors.affiliation: Department of Colorectal Surgery, Zhejiang University Jinhua Hospital, Jinhua, China.
authors.forename: Jinlin
authors.initials: J
authors.lastname: Du
mesh_terms: D000328:Adult; D001741:Black or African American / Q000706:statistics & numerical data; D000367:Age Factors; D000368:Aged; D001943:Breast Neoplasms / Q000378:metabolism / Q000401:mortality* / Q000473:pathology / Q000628:therapy; D018270:Carcinoma, Ductal, Breast / Q000378:metabolism / Q000401:mortality* / Q000473:pathology / Q000628:therapy; D018275:Carcinoma, Lobular / Q000378:metabolism / Q000401:mortality* / Q000473:pathology / Q000628:therapy; D002423:Cause of Death; D005006:Ethnicity / Q000706:statistics & numerical data; D005260:Female; D006801:Humans; D015994:Incidence; D053208:Kaplan-Meier Estimate; D016015:Logistic Models; D017533:Marital Status; D008408:Mastectomy; D008875:Middle Aged; D009367:Neoplasm Staging; D016016:Proportional Hazards Models; D018714:Radiotherapy, Adjuvant; D011960:Receptors, Estrogen / Q000378:metabolism; D011980:Receptors, Progesterone / Q000378:metabolism; D012189:Retrospective Studies; D018426:SEER Program; D015996:Survival Rate; D014481:United States
chemical_list: D011960:Receptors, Estrogen; D011980:Receptors, Progesterone
keywords: age; breast cancer; competing risk; hormone receptor; non-breast cancer-specific death; overall survival