Background: Many quality metrics of cancer care are process measures, based on whether a patient received recommended treatment for diagnosis and stage of their disease. Survival is dependent on patient characteristics, disease specifics, and care received (surgery, systemic therapies, radiation). Some argue survival is the ultimate measure of cancer care quality. Survival calculated from registry data may be a better indication of the real effects of new therapies as it contains a broad representation of patients who may not meet the eligibility criteria for clinical trials. Methods: The NCDB is composed of registry data from approximately 1,500 hospital based cancer programs, and includes stage, demographic data, co-morbidities, treatments, and vital status. Survival analyses were derived from NCDB data for all stages of breast cancer, non-small cell lung cancer (NSCLC), and pancreatic cancer. These diseases were selected because breast cancer has a high survival rate, whereas NSCLC and pancreatic cancer are diseases with a poor prognosis, but recent advances may have improved survival. Un-adjusted and risk adjusted survival were analyzed by socioeconomic, tumor, and hospital factors including stage, comorbidities, diagnosis year (to assess new treatment trends), and type of institution (academic, comprehensive community, and community cancer programs). Results: Results for these diseases and variables noted will be presented. Whereas better un-adjusted survival rates were often seen at academic cancer programs, differences disappeared after risk-adjustment. Improved survival was seen in more recent years, probably representing new treatment effects, though gains were modest and stage dependent. Conclusions: Measuring survival across hospitals and regions is critical to understanding the state of cancer treatment nationally and the effect of quality and therapy advances on patients across a variety of clinical settings. Methodologic challenges in analyzing and interpreting survival data must be realized, and continued innovation in the collection and analysis of data is needed.