A large group of patients with node-positive breast cancer was divided into a training set (n = 851) and a validation set (n = 432) to demonstrate techniques for integrating steroid hormone receptor status, DNA flow cytometric findings, and other prognostic factors to predict patient survival. Multivariate analyses showed that estrogen receptor status, the number of involved axillary lymph nodes, patient age, S-phase fraction, progesterone receptor status, and tumor size were significant predictors of survival in patients with node-positive breast cancer. Techniques for optimizing and validating a cut point for a new prognostic factor and for examining alternative representations of prognostic factors were demonstrated. Prognostic indexes were created that could be used to identify patients with very good or very poor prognoses.