Identification of high risk groups for breast cancer by means of logistic models.
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 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.
Negri
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Istituto di Biometria e Statistica Medica, Università degli Studi di Milano, Italy.
Decarli
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La Vecchia
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Marubini
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D000293:Adolescent; D000328:Adult; D000367:Age Factors; D015992:Body Mass Index; D001943:Breast Neoplasms / Q000453:epidemiology* / Q000235:genetics; D005260:Female; D006801:Humans; D007558:Italy / Q000453:epidemiology; D016015:Logistic Models; D008572:Menarche; D008593:Menopause; D008875:Middle Aged; D010298:Parity; D011336:Probability; D012044:Regression Analysis; D012307:Risk Factors