Breast Cancer Trend Analysis Using Stochastic Stimulati*

描述(由申请人提供)： 我们建议的研究的主要目标是通过与NCIS癌症干预和监测建模网络(CISNET)的合作研究协议来量化筛查和治疗干预措施对美国乳腺癌发病率和死亡率趋势的影响。在我们最初的CISNet奖项下，我们量化了筛查乳房X光检查和多药化疗对最近乳腺癌死亡率下降的相对贡献。在这项应用中，我们建议扩展我们对当前乳腺癌趋势的分析，以包括筛查到的DCIS的影响。我们还将确定乳腺癌发病率和死亡率当前趋势的组成部分，这些趋势可归因于罹患乳腺癌的高遗传风险亚群。除了更密切地研究当前的趋势外，我们还将把我们的模型的使用扩展到对未来趋势的研究。通过CISNET/DHHS补充奖，我们已经进行了一项试点研究，以确定是否可以使用我们的模型来确定2010年健康人乳腺癌死亡率目标是否可以实现。该项目表明，有必要改进我们现有的信息和通信技术网络模型，以便它可以将筛查和治疗试验的中间终点作为输入。通常，医学创新的绩效是在短期终点或替代标记物上进行评估的。在复发率降低的基础上，新的治疗方案现在被广泛采用，对其对生存的影响知之甚少。人们在提高发现率的基础上提倡新的筛查技术，但对其对生存的影响知之甚少。我们希望将乳腺癌筛查和治疗试验的中间终点外推到长期生存终点，然后将这些发现转化为人群水平。我们将把部分工作重点放在研究高危人群的新筛查技术上，以便更好地了解如何将这些干预措施转化为普通人群。我们将向更广泛的公众消费提供我们的CISNET模型，并欢迎政策制定者在研究过程中提出紧迫的问题。

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.