Open Access
February 2020 Risk Models for Breast Cancer and Their Validation
Adam R. Brentnall, Jack Cuzick
Statist. Sci. 35(1): 14-30 (February 2020). DOI: 10.1214/19-STS729


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.


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Adam R. Brentnall. Jack Cuzick. "Risk Models for Breast Cancer and Their Validation." Statist. Sci. 35 (1) 14 - 30, February 2020.


Published: February 2020
First available in Project Euclid: 3 March 2020

MathSciNet: MR4071355
Digital Object Identifier: 10.1214/19-STS729

Keywords: breast cancer , breast density , Calibration , IBIS model , risk assessment , Tyrer–Cuzick model

Rights: Copyright © 2020 Institute of Mathematical Statistics

Vol.35 • No. 1 • February 2020
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