Breast Cancer Trend Analysis Using Stochastic Stimulati*

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DESCRIPTION (provided by applicant): The main goal of our proposed research is to quantify the impact of screening and treatment interventions on breast cancer incidence and mortality trends in the United States through a collaborative research agreement with the NCIs Cancer Intervention and Surveillance Modeling Network (CISNET). Under our original CISNET award, we quantified the relative contributions of screening mammography and multiagent chemotherapy to the recent decline in breast cancer mortality. In this application, we are proposing to extend our analysis of the current breast cancer trends to include the impact of screen-detected DCIS. We will also identify the component of current trends in breast cancer incidence and mortality attributable to the subpopulation at high genetic risk for developing the disease. In addition to studying the current trends more closely, we will extend the use of our model to the study of future trends. Through a CISNET/DHHS supplemental award, we have already performed a pilot study on the use of our model in determining whether or not the Healthy People 2010 goals in breast cancer mortality could be achieved. This project revealed the need to enhance our existing CISNET model so that it could take as inputs intermediate endpoints from screening and treatment trials. More often than not the performance of medical innovations are being evaluated on short term endpoints or surrogate markers. New treatment protocols are now being broadly adopted on the basis of lowered recurrence rates, with little knowledge of their impact on survival. New screening technologies are being advocated on the basis of increased detection rates, with little knowledge of their impact on survival. We want to extrapolate the intermediate endpoints of breast cancer trials in screening and treatment to long term survival endpoints and then translate these findings to the population level. We will focus a part of our efforts on the study of new screening technologies in the high risk population in order to better understand how these interventions can be translated to the general population. We will make our CISNET model available for broader public consumption and welcome pressing questions from policy makers during the course of our study.