Modeling to Minimize Detection Bias in Cancer Risk Prediction Studies

项目概要/摘要 癌症风险预测是发展针对性癌症预防和筛查的关键一步 施政纲要而人们越来越意识到，癌症风险预测研究可能会因检测偏倚而失真， 特别是在筛选的人群中。当筛查和诊断模式不同时， 与潜在危险因素的关联。检测偏倚可能夸大或削弱估计的疾病风险因素 协会，并可能对我们制定健全的预防和筛查政策的能力产生不利影响。 该应用程序的目的是改变癌症检测偏倚的评估和解决方式 风险预测我们将利用疾病自然史建模技术来消除潜在风险 从观察到的筛查和诊断历史中发现疾病。我们将严格调查 疾病建模，以减少检测偏倚，并将应用我们的方法来评估和解决检测偏倚 这可能已经影响了前列腺癌和乳腺癌的早期检测指南。我们将传播我们的 通过在线用户界面建立模型，使研究人员能够在筛选的 评估他们的研究对检测偏倚的敏感性。最后，我们将研究检测的影响 通过对前列腺癌筛查的概念验证研究，对政策相关结果的偏见。 我们的具体目标如下：目标1 [方法开发]：开发和验证癌症模型 基于筛选人群的风险预测研究中评估和减少检测偏倚的方法目的 2 [乳腺密度应用]：应用目标1中开发的方法评估和补救任何检测 发表的乳腺密度和乳腺癌风险之间的关联的偏差。尽管重大政策 乳腺密度导致乳腺癌诊断风险升高的研究结果的影响，这些研究结果 从未被询问过检测偏倚;目标3 [软件传播]：开发、测试和部署一个 在线用户界面，允许研究人员在筛选的患者中进行癌症风险预测研究 人群以评估潜在的检测偏差;目标4 [政策影响]：评估检测偏差的影响 关于候选前列腺癌筛查政策的利弊权衡，作为翻译的概念证明 对政策设置的检测偏差。 该应用程序将开创性地使用疾病建模作为解决偏倚来源的工具， 目前在广泛的政策驱动的癌症风险预测。调查员小组由以下人员组成： 领导研究人员开发用于早期检测的疾病模型。拟议的工作将产生 这是迄今为止对检测偏差的工作方式以及如何在实践中解决它的最严格的分析。

Project Summary/Abstract Cancer risk prediction is a critical step towards the development of targeted cancer prevention and screening policies. There is a growing awareness that cancer risk prediction studies may be distorted by detection bias, particularly in screened populations. Detection bias occurs when screening and diagnostic patterns vary in association with potential risk factors. Detection bias can exaggerate or attenuate estimated disease-risk factor associations and may adversely affect our ability to develop sound prevention and screening policies. The objective of this application is to change the way that detection bias is assessed and addressed in cancer risk prediction. We will harness the technique of disease natural history modeling to decouple the underlying risk of disease from observed screening and diagnosis histories. We will rigorously investigate the performance of disease modeling to reduce detection bias and will apply our approach to assess and address detection bias that may already be impacting early detection guidelines in prostate and breast cancer. We will disseminate our models via an online user interface that will permit investigators conducting risk prediction studies in screened populations to assess their studies' susceptibility to detection bias. Finally, we will study the impact of detection bias on policy-relevant outcomes via a proof-of-concept study of prostate cancer screening. Our specific aims are as follows: Aim 1 [Methods development]: Develop and validate a cancer modeling method for assessing and reducing detection bias in risk prediction studies based on screened populations; Aim 2 [Breast density application]: Apply the method developed in Aim 1 to assess and remediate any detection bias in published associations between breast density and breast cancer risk. Despite the major policy implications of findings that breast density leads to an elevated risk of breast cancer diagnosis, these findings have never been interrogated for detection bias; Aim3 [Software dissemination]: Develop, test, and deploy an online user interface that will permit investigators conducting cancer risk prediction studies in screened populations to assess the potential detection bias; Aim 4 [Policy impact]: Assess the impact of detection bias on harm-benefit tradeoffs of candidate prostate cancer screening policies as a proof of concept for the translation of detection bias to the policy setting. This application will pioneer the use of disease modeling as tool for addressing a source of bias that may be present across a wide range of policy-driving cancer risk predictions. The investigator team is comprised of leading investigators in the development of disease models for early detection. The proposed work will produce the most rigorous analysis to date of the way that detection bias works and how it may be addressed in practice.