Comparison of Questionnaire-Based Breast Cancer Prediction Models in the Nurses' Health Study

Background: The Gail model and the model developed by Tyrer and Cuzick are two questionnaire-based approaches with demonstrated ability to predict development of breast cancer in a general population. Methods: We compared calibration, discrimination, and net reclassification of these models, using data from questionnaires sent every 2 years to 76,922 participants in the Nurses' Health Study between 1980 and 2006, with 4,384 incident invasive breast cancers identified by 2008 (median follow-up, 24 years; range, 1-28 years). In a random one third sample of women, we also compared the performance of these models with predictions from the Rosner-Colditz model estimated from the remaining participants. Results: Both the Gail and Tyrer-Cuzick models showed evidence of miscalibration (Hosmer-Lemeshow P < 0.001 for each) with notable (P < 0.01) overprediction in higher-risk women (2-year risk above about 1%) and underprediction in lower-risk women (risk below about 0.25%). The Tyrer-Cuzick model had slightly higher C-statistics both overall (P < 0.001) and in age-specific comparisons than the Gail model (overall C, 0.63 for Tyrer-Cuzick vs. 0.61 for the Gail model). Evaluation of net reclassification did not favor either model. In the one third sample, the Rosner-Colditz model had better calibration and discrimination than the other two models. All models had C-statistics <0.60 among women ages >= 70 years. Conclusions: Both the Gail and Tyrer-Cuzick models had some ability to discriminate breast cancer cases and noncases, but have limitations in their model fit. Impact: Refinements may be needed to questionnaire-based approaches to predict breast cancer in older and higher-risk women.