Abstract
Strategies to prevent cancer and diagnose it early when it is most treatable are needed to reduce the public health burden from rising disease incidence. Risk assessment is playing an increasingly important role in targeting individuals in need of such interventions. For breast cancer many individual risk factors have been well understood for a long time, but the development of a fully comprehensive risk model has not been straightforward, in part because there have been limited data where joint effects of an extensive set of risk factors may be estimated with precision. In this article we first review the approach taken to develop the IBIS (Tyrer–Cuzick) model, and describe recent updates. We then review and develop methods to assess calibration of models such as this one, where the risk of disease allowing for competing mortality over a long follow-up time or lifetime is estimated. The breast cancer risk model model and calibration assessment methods are demonstrated using a cohort of 132,139 women attending mammography screening in the State of Washington, USA.
Highlights
Unlike lung cancer and cervix cancer where a single factor explains the majority of the cases, a large number of factors have been found to be important for determining the risk of breast cancer
For breast cancer many individual risk factors have been well understood for a long time, but the development of a fully comprehensive risk model has not been straightforward, in part because there have been limited data where joint effects of an extensive set of risk factors may be estimated with precision
An increased risk in women with a family history appears to have been known in ancient Roman times [59], and in 1842 RigoniStern reported that nuns had an increased risk of breast cancer [53, 26], paving the way for further research which established that not having a child or having a first childbirth at an older age increased the risk of this disease
Summary
Unlike lung cancer (tobacco smoking) and cervix cancer (persistent infection with the human papilloma virus) where a single factor explains the majority of the cases, a large number of factors have been found to be important for determining the risk of breast cancer. In 1976, Wolfe discovered that breast density was another key risk factor that was broadly unrelated to the known classical factors, but of roughly equal importance in their combined predictive value [69]. This has been further developed by Boyd [11, 10], McCormack and others [45]. Multiple modes of failure are conveniently characterised using functions for the rate at which each cause J occurs at each follow-up time, given that the person has not yet died, or experienced a specific event of interest.
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