Diagnostic accuracy of the Gail model in the Black Women's Health Study

Journal Name: 
Breast J
Authors: 
Adams-Campbell,L.L.
Makambi,K.H.
Palmer,J.R.
Rosenberg,L.
Abstract: 
The Gail model is used to predict the risk of breast cancer in women of diverse race/ethnic groups for clinical trial protocols. However, this model has only been validated in US white women. Using a nested case-control study design, we evaluated the diagnostic accuracy of the original Gail model (GM) and that of the revised Gail model algorithm for blacks/African-Americans (GM-B) in the Black Women's Health Study (BWHS). Risk profiles were derived via a self reported questionnaire at the time of enrollment into the BWHS in 1995. Biennial questionnaires were obtained from the participants to determine the incident cases of breast cancer. The study of 725 breast cancer cases and 725 controls revealed that the 5-year risk of breast cancer based on the GM ranged from 0.2% to 15.4% among cases and 0.2% to 13.6% among the controls. Based on the GM-B, the 5-year risk of breast cancer ranged from 0.2% to 8.7% among cases and 0.2% to 7.2% among the controls. The sensitivities of the GM and GM-B model with the standard cutoff of 1.7% were 17.9% (95% CI: 15.9-19.9%) and 4.1% (95% CI: 3.0-5.2), respectively. Both the original and the modified version of the Gail model underestimate the risk of developing breast cancer in African-American women. More importantly, the modified Gail Model (GM-B) does a worse job at predicting the development of breast cancer for blacks than the original model (GM)
7
2007
Volume: 
13
Pages: 
332-336
Keywords: 
Adult, African American, African Americans, African-American, Aged, Algorithms, Blacks, Breast, Breast Neoplasms, Case-Control Studies, clinical, clinical trial, Development, diagnostic, etiology, evaluation, Evaluation Studies, Female, genetics, Health, Humans, Maternal Age, Methods, Middle Aged, Models,Statistical, Postmenopause, Predictive Value of Tests, Premenopause, Questionnaires, Research, Research Support, Risk, Risk Assessment, ROC Curve, Standards, support, Universities, women, Women's Health