Modeling to Minimize Detection Bias in Cancer Risk Prediction Studies

建立模型以最大限度地减少癌症风险预测研究中的检测偏差

基本信息

批准号：
10246991
负责人：
RUTH D ETZIONI
金额：
$ 5.86万
依托单位：
FRED HUTCHINSON CANCER RESEARCH CENTER
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-09-19 至 2022-03-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10246991
关键词：
Address Affect Attenuated Automobile Driving Awareness Behavioral Biological Biopsy Breast Breast Cancer Detection Breast Cancer Risk Factor Breast Cancer Surveillance Consortium Cancer Model Complement Computer software Data Detection Development Diagnosis Diagnostic Disease Disease model Early Diagnosis Ethnic Origin Family Frequencies Genetic Gold Guidelines Individual Laws Lead Learning Malignant Neoplasms Malignant neoplasm of lung Malignant neoplasm of prostate Methods Modeling Neighborhoods Notification Outcome Pattern Performance Policies Population Population Surveillance Predisposition Prevention Prostate Prostate Cancer Prevention Trial Provider Published Comment Publishing Race Recommendation Recording of previous events Registries Research Personnel Risk Risk Estimate Risk Factors Role Screening for Prostate Cancer Screening for cancer Selenium Smoking History Socioeconomic Status Software Tools Source Sun Exposure Techniques Testing Time Translations Validation Vitamin E Woman Work base breast cancer diagnosis breast density cancer diagnosis cancer prevention cancer risk design disease natural history disorder risk individualized prevention malignant breast neoplasm melanoma method development novel policy implication preference prostate cancer risk risk prediction risk prediction model risk stratification screening screening policy sociodemographics sound tool

项目摘要

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.

项目概要/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因检测偏差而扭曲，特别是在筛查人群中。当筛查和诊断模式不同时，就会出现检测偏差与潜在危险因素的关联。检测偏差可能会夸大或减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。此应用程序的目的是改变癌症中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建模技术来解耦潜在的风险从观察到的筛查和诊断历史中了解疾病的情况。我们将严格调查其表现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建立模型，允许研究人员在筛选的情况下进行风险预测研究人群来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产生偏见。我们的具体目标如下：目标 1 [方法开发]：开发并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差的方法；目的 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腺密度与乳腺癌风险之间的关联存在偏见。尽管有重大政策乳腺密度导致乳腺癌诊断风险升高的发现的含义，这些发现从未因检测偏差而受到讯问；目标3 [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的情况下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候选前列腺癌筛查政策的危害与效益权衡作为翻译概念的证明政策设置的检测偏差。该应用程序将开创使用疾病模型作为解决可能存在的偏见来源的工具。存在于广泛的政策驱动的癌症风险预测中。研究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。