Breast Cancer Trend Analysis Using Stochastic Stimulati*

使用随机刺激进行乳腺癌趋势分析*

• 批准号: 7286736
• 负责人: SYLVIA KATINA PLEVRITIS
• 金额: $ 33.85万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2000
• 资助国家: 美国
• 起止时间: 2000-09-01 至 2010-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7286736
• 关键词: Address; Adopted; Advocate; Age; Agreement; Award; BRCA1 gene; BRCA2 gene; Breast; Cancer Control; Cancer Intervention and Surveillance Modeling Network; Collaborations; Condition; Consumption; Data; Detection; Disease; Disease Progression; Documentation; Early Diagnosis; End Point; Family history of; Female; Future; General Population; Genetic Predisposition to Disease; Genetic Risk; Goals; Healthy People 2010; Incidence; Intervention; Knowledge; Magnetic Resonance Imaging; Malignant Neoplasms; Mammography; Medical; Modality; Modeling; Modification; Mutation; Natural History; Noninfiltrating Intraductal Carcinoma; Performance; Phase; Pilot Projects; Policy Maker; Population; Protocols documentation; Rate; Recurrence; Relative (related person); Research; Risk; Running; Screening procedure; Simulate; Source Code; Specific qualifier value; Surrogate Markers; Technology; Translating; Treatment Protocols; United States; Work; attributable mortality; base; chemotherapy; innovation; malignant breast neoplasm; member; models and simulation; mortality; network models; programs; treatment trial; trend; web interface

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.

描述（由申请人提供）： 我们提出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，量化筛查和治疗干预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，我们量化了筛查性乳腺X射线摄影和多药化疗对最近乳腺癌死亡率下降的相对贡献。在本申请中，我们建议扩展我们对当前乳腺癌趋势的分析，以包括筛查发现的DCIS的影响。我们还将确定目前乳腺癌发病率和死亡率趋势的组成部分，这些趋势可归因于发生乳腺癌的高遗传风险亚群。除了更仔细地研究当前趋势外，我们还将扩展模型的使用范围，以研究未来趋势。通过CISNET/DHHS补充奖，我们已经进行了一项试点研究，使用我们的模型来确定是否可以实现2010年健康人群乳腺癌死亡率的目标。该项目揭示了增强我们现有CISNET模型的必要性，以便它可以将筛选和治疗试验的中间终点作为输入。医疗创新的表现往往是根据短期终点或替代标志物进行评估的。新的治疗方案目前正被广泛采用，其基础是降低复发率，但对它们对生存率的影响知之甚少。新的筛查技术是在提高检测率的基础上提倡的，但对它们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。我们将集中精力研究高危人群中的新筛查技术，以便更好地了解这些干预措施如何适用于普通人群。我们将使我们的CISNET模型可供更广泛的公众消费，并欢迎政策制定者在研究过程中提出紧迫的问题。