Estimating Overdiagnosis in Cancer Screening Studies

 描述(由申请人提供)：项目摘要/摘要每年有数百万美国男性和女性接受癌症早期检测筛查。过度诊断，或通过筛查永远不会被临床诊断的病例来检测，现在被认为是筛查的最大潜在危害。这给乳腺癌和前列腺癌带来了一个特别困难的临床两难境地，前者筛查可发现许多原位肿瘤，后者潜伏性病例在老年人群中非常普遍。过度诊断的病例无法通过治疗得到帮助，而过度治疗这些病例在患者发病率和经济成本方面都会付出高昂的代价。有关过度诊断的知识对于制定良好的筛查政策和做出知情的患者决策至关重要。然而，过度诊断取决于筛查实践和个人因素，许多已发表的研究是有偏见的，或者不适用于不同于用于估计的人群。这项工作的目的是增进关于如何有效地估计过度诊断的知识，并提供与特定癌症筛查环境相关的过度诊断的具体信息，以便为筛查政策的制定和临床决策提供信息。在我们的第一个目标中，我们将进行一项模拟研究，以确定有效的过度诊断研究的可接受方法和关键特征。这一目标的经验教训将应用于我们的第二个目标，它将调整已建立的人口模型，以估计不同筛查政策下与乳腺癌和前列腺癌筛查相关的过度诊断率，以及根据患者和肿瘤特征定义的不同人口亚组。这些估计将通过我们的第三个目标提供给政策制定者和临床医生，该目标将开发在线计算器来呈现 向这些群体提供有关过度诊断的相关和有用信息。我们的研究团队由统计学家、乳腺癌和前列腺癌建模专家和临床医生组成，其中包括模拟建模专家、杰出的临床研究人员以及乳腺癌和前列腺癌筛查政策制定方面的领导者。这项工作将推动该领域在如何估计过度诊断以及如何告诉负责共享和知情决策的政策制定者和临床医生方面达成更大共识。这一应用所产生的知识将导致合理的筛查政策和更知情的临床决策，并应在改善与癌症筛查相关的利与弊的平衡的持续斗争中提供宝贵的量化工具。

 DESCRIPTION (provided by applicant): Project Summary/Abstract Millions of US men and women undergo screening for early cancer detection every year. Overdiagnosis, or the detection by screening of cases that would never become clinically diagnosed, is now recognized as the greatest potential harm of screening. This presents an especially difficult clinical dilemma in breast cancer, where screening detects many in-situ tumors, and in prostate cancer, where latent cases are highly prevalent in older age groups. Overdiagnosed cases cannot be helped by treatment and overtreatment of these cases carries a high price in terms of patient morbidity and economic costs. Knowledge about overdiagnosis is critical for well-formed screening policies and for well-informed patient decision making. However, overdiagnosis depends on screening practices and personal factors and many published studies are biased or do not apply to populations that differ from those used for estimation. The objective of this work is to advance knowledge about how to validly estimate overdiagnosis and to provide concrete information about overdiagnosis associated with specific cancer screening settings so as to inform screening policy development and clinical decision making. In our first aim we will conduct a simulation study to identify acceptable approaches and key characteristics of valid overdiagnosis studies. Lessons from this aim will be applied in our second aim, which will adapt established population models to estimate overdiagnosis rates associated with breast and prostate cancer screening under different screening policies and for different population subgroups defined by patient and tumor characteristics. These estimates will be made available to policy makers and clinicians via our third aim, which will develop online calculators to present relevant and useful information about overdiagnosis to these groups. Our study team of statisticians, breast and prostate cancer modelers and clinicians includes experts in simulation modeling, prominent clinical researchers and leaders in policy development for breast and prostate cancer screening. This work will move the field forward to a point of greater consensus about how to estimate overdiagnosis and what to tell policy makers and clinicians who have been tasked with shared and informed decision making. The knowledge generated by this application will lead to sound screening policies and better- informed clinical decisions and should provide a valuable quantitative tool in the ongoing battle to improve the balance of benefit and harm associated with cancer screening.