Extensions of the Rosner-Colditz breast cancer prediction model to include older women and type-specific predicted risk. Academic Article uri icon

Overview

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.

publication date

  • June 6, 2017

Research

keywords

  • Breast Neoplasms
  • Models, Statistical

Identity

PubMed Central ID

  • PMC5560077

Scopus Document Identifier

  • 85020294902

Digital Object Identifier (DOI)

  • 10.1093/jnci/dju397

PubMed ID

  • 28589369

Additional Document Info

volume

  • 165

issue

  • 1