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早期癌症检测筛查。过度诊断，或通过筛查发现永远不会成为临床诊断的病例，现在被认为是筛查的最大潜在危害。这在乳腺癌和前列腺癌中提出了一个特别困难的临床困境，在乳腺癌中，筛查检测到许多原位肿瘤，在前列腺癌中，潜伏病例在老年人群中非常普遍。过度诊断的病例不能通过治疗得到帮助，这些病例的过度治疗在患者发病率和经济成本方面付出了高昂的代价。关于过度诊断的知识对于完善的筛查政策和充分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，许多已发表的研究存在偏见，或不适用于与用于估计的人群不同的人群。这项工作的目的是推进有关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，我们将进行模拟研究，以确定可接受的方法和有效的过度诊断研究的关键特征。从这一目标中获得的经验教训将应用于我们的第二个目标，这将调整已建立的人群模型，以估计在不同的筛查政策下，以及根据患者和肿瘤特征定义的不同人群亚组中与乳腺癌和前列腺癌筛查相关的过度诊断率。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， 向这些群体提供关于过度诊断的相关和有用的信息。我们的统计学家，乳腺癌和前列腺癌建模师和临床医生的研究团队包括模拟建模专家，杰出的临床研究人员和乳腺癌和前列腺癌筛查政策制定的领导者。这项工作将使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的共识。该应用程序产生的知识将导致健全的筛查政策和更明智的临床决策，并应在正在进行的战斗中提供有价值的定量工具，以改善与癌症筛查相关的利益和伤害的平衡。

 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.