Breast Cancer Trend Analysis Using Stochastic Stimulati*

我们研究的主要目标是量化筛查和治疗干预对乳腺癌的影响。 通过与NCI的合作研究协议， 癌症干预和监测模型网络（CISNET）。根据我们最初的CISNET奖项，我们量化了 筛查性乳房X光检查和多药化疗对近期乳腺癌发病率下降的相对影响 癌症死亡率在本申请中，我们建议将我们对当前乳腺癌趋势的分析扩展到 包括屏幕检测DCIS的影响。我们还将确定乳腺癌当前趋势的组成部分 发病率和死亡率归因于高遗传风险的亚群。此外， 为了更深入地研究当前的趋势，我们将把我们的模型用于研究未来的趋势。通过 CISNET/DHHS补充合同，我们已经进行了一项试点研究，使用我们的模型来确定 whether是否or not the Healthy健康People人2010 goals目标in breast乳腺癌mortality死亡could be achieved实现.该项目揭示了 我需要增强我们现有的CISNET模型，以便它可以从筛选和 治疗试验。医疗创新的绩效往往是在短期内评估的 终点或替代标记。新的治疗方案目前正在广泛采用的基础上降低 复发率，很少了解其对生存的影响。新的筛选技术正在被提倡， the basis基础of increased增加detection检测rates率，with little knowgeof their知识of impact影响on survival生存.我们想推断出 从筛查和治疗的乳腺癌试验的中间终点到长期生存终点，然后 将这些发现转化为人口水平。我们将集中一部分精力研究新的筛查方法， 为了更好地了解如何将这些干预措施转化为 我们将使我们的CISNET模型可用于更广泛的公众消费，并欢迎迫切 在我们的研究过程中，政策制定者提出了一些问题。

The main goal of our proposed research is to quantify the impact of screeningand treatment interventionson breast cancer incidenceand 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 relativecontributionsof 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 attributableto the subpopulation at high genetic risk for developingthe disease. In additionto 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 mortalitycould be achieved. This project revealed the need to enhance our existing CISNET model so that it could take as inputs intermediate endpointsfrom 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 knowledgeof 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 availablefor broader publicconsumption and welcome pressing questions from policy makers during the course of our study.