Dr. Gail received an M.D. from Harvard Medical School in 1968 and a Ph.D. in statistics from George Washington University in 1977. He joined NCI in 1969 and became Chief of the Biostatistics Branch in 1994. Dr. Gail is a Fellow and former President of the American Statistical Association, a former President of the Eastern North American Region of the International Biometrics Society (ENAR), a Fellow of the American Association for the Advancement of Science, an elected member of the American Society for Clinical Investigation, and an elected member of the Institute of Medicine. He has received the Spiegelman Gold Medal for Health Statistics, the Snedecor Award for applied statistical research, the Howard Temin Award for AIDS Research, the NIH Director’s Award, and the PHS Distinguished Service Medal. Dr. Gail’s research interests include developing statistical methods for the design and analysis of epidemiologic studies and developing models to predict the absolute risk of disease.

Abstract

The value of adding single nucleotide polymorphism data to a model that predicts breast cancer risk

Seven single nucleotide polymorphisms (SNPs) have recently been confirmed to be associated with breast cancer. I assessed the value of adding these SNPs to the Breast Cancer Risk Assessment Tool (BCRAT), which is based on ages at menarche and at first live birth, family history of breast cancer, and history of breast biopsy examinations. The model with these SNPs (BCRATplus7) had an area under the receiver operating characteristic curve (AUC) of 0.607, compared to 0.632 for BCRAT. This improvement is less than from adding mammographic density to BCRAT. I also assessed how much BCRATplus7 reduced expected losses in deciding whether a woman should take tamoxifen to prevent breast cancer and in deciding whether a woman should have a mammogram. In addition, I examined whether BCRATplus7 was more effective than BCRAT in allocating a scarce public health resource, such as access to mammography, based on ranking women on their breast cancer risk and allocating the resource to those at highest risk. In none of these applications did BCRATplus7 perform substantially better than BCRAT. I conclude that the available SNPs do not improve the performance of models to estimate breast cancer risk enough to warrant their use outside the research setting.