Modeling to Minimize Detection Bias in Cancer Risk Prediction Studies

项目摘要/摘要 癌症风险预测是发展有针对性的癌症预防和筛查的关键一步 政策。人们越来越意识到，癌症风险预测研究可能会受到检测偏差的扭曲， 特别是在经过筛查的人群中。当筛查和诊断模式不同时，会出现检测偏差 与潜在风险因素有关。检测偏差可能会夸大或减弱估计的疾病风险因素 这可能会对我们制定健全的预防和筛查政策的能力产生不利影响。 这项应用的目标是改变癌症患者评估和解决检测偏差的方式 风险预测。我们将利用疾病自然史模拟技术来分离潜在风险 从观察到的筛查和诊断病史中发现疾病。我们将严格调查 疾病建模以减少检测偏差，并将应用我们的方法来评估和解决检测偏差 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 通过在线用户界面进行模型，允许调查人员在筛选的 评估他们的研究对检测偏差的敏感性。最后，我们将研究检测的影响 通过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 在基于筛查人群的风险预测研究中评估和减少检测偏差的方法 2[乳房密度应用]：应用目标1中开发的方法来评估和补救任何检测 已发表的乳房密度和乳腺癌风险之间的关联存在偏见。尽管有重大政策 乳房密度导致乳腺癌诊断风险增加的发现的含义 从未因检测偏差而受到询问；Aim3[软件传播]：开发、测试和部署 在线用户界面，允许研究人员在筛选中进行癌症风险预测研究 评估潜在检测偏差的人群；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 检测偏差对策略设置的影响。 这项应用将率先使用疾病建模作为工具来解决可能存在的偏差来源 呈现在广泛的政策驱动的癌症风险预测中。调查组由以下人员组成 领导研究人员开发早期发现的疾病模型。拟议的工作将产生 迄今为止对检测偏差的工作方式以及在实践中可能如何解决的最严格的分析。

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.