Logistic regression is commonly used to develop mathematical equations that accurately describe a set of observations that have been made in a group of individuals. A very simple example is the Finnish BRCA gene mutation prediction model, which was developed using data from 148 BRCA gene mutation-tested families with a strong history of breast and/or ovarian cancer.12 This model uses the earliest age at breast cancer diagnosis and the number of ovarian cancers in the family to calculate the probability that the family carries a deleterious mutation in BRCA1 or BRCA2. This is the most austere logistic regression model, but also one of the most intelligent as these two variables form the core of nearly every other BRCA gene mutation prediction model. The Gail model is, perhaps, the best known and most widely used logistic regression model for calculating breast cancer risk.10 This model was developed from a case-control study that included nearly 6000 women who had participated in the Breast Cancer Detection Demonstration Project. Dozens of risk factors were evaluated to identify the smallest number of factors providing the best risk prediction. Risk factors included in the final model were age, age at menarche, age at first live birth, number of biopsies, any history of atypical hyperplasia, and number of first-degree female relatives with breast cancer. The initial model was applicable to Caucasian women only and included both invasive and in situ cancer as end points. In preparing for the P1 Tamoxifen Breast Cancer Prevention Trial (BCPT), investigators with the National Surgical Adjuvant Breast and Bowel Project (NSABP) modified the original Gail model to include African American women and to limit the probability calculation to invasive breast cancer only.6 This is the model in common use today and is known as the modified Gail model or Gail2 model. Gail and colleagues have since described a revised model that excludes age at menarche but adds body weight and mammographic density.13 Barlow and colleagues used data from 1,007,600 women enrolled in the Breast Cancer Screening Consortium to develop separate logistic regression models for premenopausal and postmenopausal women.14 The model for premenopausal women includes age, prior breast surgery, family history of breast cancer in first-degree relatives, and mammographic density. The model for postmenopausal women includes these same factors with the addition of ethnicity, race, body mass index, age at first live birth, hormone therapy use, history of oophorectomy, and history of a "false-positive" mammogram in the prior year. Interaction between risk factors was not considered in this model.