Identification of high risk groups for breast cancer by means of logistic models

Eva Negri, Adriano Decarli, Carlo La Vecchia, Ettore Marubini

Research output: Contribution to journalArticlepeer-review


In order to identify high risk groups for breast cancer, unconditional multiple logistic regression models based on 5 widely recognized and easily identifiable risk factors (age at menarche, at menopause and at first birth, family history of breast cancer and body mass index) were applied to a large dataset including 2085 cases and 1936 controls aged 50 or over derived from two unmatched hospital-based case-control studies conducted in Italy. Although various models provided an excellent fitting, both on the whole dataset and using a training-testing approach to an a priori identified separate subset, the observed extent of variation in breast cancer risk between highest and lowest decile of the distribution was limited to a factor 2. This indicates that the 5 variables considered did not allow identification of subgroups with substantially elevated risk of breast cancer to have practical implications for screening/prophylactic treatment purposes.

Original languageEnglish
Pages (from-to)413-418
Number of pages6
JournalJournal of Clinical Epidemiology
Issue number5
Publication statusPublished - 1990


  • Age at first live-birth
  • Body weight
  • Breast neoplasms
  • High risk groups
  • Menarche
  • Menopause

ASJC Scopus subject areas

  • Medicine(all)
  • Epidemiology
  • Public Health, Environmental and Occupational Health


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