2019/07/09 by Adam R Brentnall, Adam R. Brentnall, Jack Cuzick · 71 citations
Biochemistry, Genetics and Molecular Biology · Mathematics · Medicine · #BRCA gene mutations in cancer #Breast cancer #Cancer #Cancer Risks and Factors #Cohort #Cohort study #Disease #Estimation #Global Cancer Incidence and Screening #Ibis #Mammography #Risk assessment #msc:62 #msc:62-02 #msc:62N01 #msc:62P10 #stat.AP #stat.ME
paper · pdf · doi:10.1214/19-sts729
published in Statistical Science 35(1), 14-30 (Institute of Mathematical Statistics)
arxiv created 2019/07/09 · openalex created_date 2019/07/12 · openalex publication_date 2020/02/01 · arxiv updated 2020/03/09 · openalex updated_date 2026/08/06
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.