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Breast Cancer Research and Treatment

, Volume 165, Issue 1, pp 215–223 | Cite as

Extensions of the Rosner-Colditz breast cancer prediction model to include older women and type-specific predicted risk

  • Robert J. GlynnEmail author
  • Graham A. Colditz
  • Rulla M. Tamimi
  • Wendy Y. Chen
  • Susan E. Hankinson
  • Walter W. Willett
  • Bernard Rosner
Epidemiology

Abstract

Purpose

A breast cancer risk prediction rule previously developed by Rosner and Colditz has reasonable predictive ability. We developed a re-fitted version of this model, based on more than twice as many cases now including women up to age 85, and further extended it to a model that distinguished risk factor prediction of tumors with different estrogen/progesterone receptor status.

Methods

We compared the calibration and discriminatory ability of the original, the re-fitted, and the type-specific models. Evaluation used data from the Nurses’ Health Study during the period 1980–2008, when 4384 incident invasive breast cancers occurred over 1.5 million person-years. Model development used two-thirds of study subjects and validation used one-third.

Results

Predicted risks in the validation sample from the original and re-fitted models were highly correlated (ρ = 0.93), but several parameters, notably those related to use of menopausal hormone therapy and age, had different estimates. The re-fitted model was well-calibrated and had an overall C-statistic of 0.65. The extended, type-specific model identified several risk factors with varying associations with occurrence of tumors of different receptor status. However, this extended model relative to the prediction of any breast cancer did not meaningfully reclassify women who developed breast cancer to higher risk categories, nor women remaining cancer free to lower risk categories.

Conclusions

The re-fitted Rosner-Colditz model has applicability to risk prediction in women up to age 85, and its discrimination is not improved by consideration of varying associations across tumor subtypes.

Keywords

Prediction Models, statistical Calibration Discrimination Reclassification 

Notes

Funding

This project was funded by a cohort infrastructure Grant (UM1 CA186107), and a program project Grant (P01 CA87969) from the National Cancer Institute.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

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Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.Channing Division of Network MedicineBrigham and Women’s Hospital and Harvard Medical SchoolBostonUSA
  2. 2.Department of BiostatisticsHarvard T.H. Chan School of Public HealthBostonUSA
  3. 3.Division of Preventive MedicineBrigham and Women’s Hospital and Harvard Medical SchoolBostonUSA
  4. 4.Alvin J. Siteman Cancer Center and Department of Surgery, Division of Public Health Sciences, School of MedicineWashington University of St. LouisSt. LouisUSA
  5. 5.Department of EpidemiologyHarvard T.H. Chan School of Public HealthBostonUSA
  6. 6.Dana-Farber Cancer InstituteBostonUSA
  7. 7.Division of Biostatistics and Epidemiology, School of Public Health SciencesUniversity of MassachusettsAmherstUSA
  8. 8.Department of NutritionHarvard T.H. Chan School of Public HealthBostonUSA

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