Review

What can be learnt from models of incidence rates?

Graham A Colditz^* and Bernard A Rosner

• * Corresponding author: Graham A Colditz graham.colditz@channing.harvard.edu

Author Affiliations

Cancer Epidemiology Program, and the Biostatistics Program, Dana-Farber/Harvard Cancer Center, and Channing Laboratory, Department of Medicine, Brigham and Women's Hospital, and Harvard Medical School, Boston, MA, USA

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Breast Cancer Research 2006, 8:208 doi:10.1186/bcr1414

Published: 6 June 2006

Abstract

Models of breast cancer incidence have evolved from the observation by Armitage and Doll in the 1950s that the pattern of incidence by age differs for reproductive cancers from those of other major malignancies. Both two-stage and multistage models have been applied to breast cancer incidence. Consistent across modeling approaches, risk accumulation or the rate of increase in breast cancer incidence is most rapid from menarche to first birth. Models that account for the change in risk after menopause and the temporal sequence of reproductive events summarize risk efficiently and give added insights to potentially important mechanistic features. First pregnancy has an adverse impact on progesterone receptor negative tumors, while increasing parity reduces the risk of estrogen/progesterone receptor positive tumors but not estrogen/progesterone receptor negative tumors. Integrated prediction models that incorporate prediction of carrier status for highly penetrant genes and also account for lifestyle factors, mammographic density, and endogenous hormone levels remain to be efficiently implemented. Models that both inform and reflect the emerging understanding of the molecular and cell biology of carcinogenesis are still a long way off.