Statistical Methods for Cancer Absolute Risk Prediction

癌症绝对风险预测的统计方法

• 批准号: 9052041
• 负责人: Jinbo Chen
• 金额: $ 33.85万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-04-01 至 2019-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9052041
• 关键词: Address; Age; American College of Radiology Imaging Network; Area; Breast; Breast Cancer Risk Assessment Tool; Cancer Model; Cancer Prognosis; Case Study; Case-Control Studies; Communities; Computer software; Coupled; Data; Development; Digital Mammography; Disease; Equilibrium; Evaluation; Goals; Health; Image; Incidence; Individual; Malignant Neoplasms; Mammographic Density; Measures; Methods; Modeling; NIH Program Announcements; National Cancer Institute; Nested Case-Control Study; Performance; Phase; Population; Prevention; Research Design; Research Personnel; Research Support; Risk; Risk Factors; Sampling; Statistical Methods; Update; Work; base; breast density; cancer risk; case control; clinical application; cost; cost effective; design; hazard; high risk; innovation; malignant breast neoplasm; mortality; novel; predictive modeling; screening; tool; user friendly software

DESCRIPTION (provided by applicant): Development, Application, and Evaluation of Prediction Models for Cancer Risk and Prognosis issued by the National Cancer Institute (NCI) supports research on innovative statistical methods for developing and evaluating new and existing cancer risk prediction models. Predictive accuracy is a key factor for determining clinical applicability of cancer risk prediction models in identifying high-risk individuals for ealy prevention. With continuously emerging risk factors for cancer, there is a pressing need for timely assessment of their added values for prediction. But study designs that balance cost and statistical efficiency and accompanying statistical methods are mostly lacking. This proposal responds to this PA by developing and evaluating cost-effective two-phase stratified case-control study designs and statistical inference methods for developing and evaluating absolute risk prediction models for cancer. Our proposed work is built on a popular method that integrates case-control data and external hazard rates of cancer and mortality to predict cancer absolute risk. As a showcase example for our statistical methods and accompanying software, we explore the added value of volumetric breast density for predicting breast cancer risk.

描述（由申请人提供）：国家癌症研究所（NCI）发布的癌症风险和预后预测模型的开发，应用和评估支持开发和评估新的和现有癌症风险预测模型的创新统计方法研究。预测精度是确定癌症风险预测模型在识别高风险个体以进行预防的临床适用性的关键因素。由于癌症的不断出现的风险因素，迫切需要及时评估其附加值的预测值。但是，研究设计平衡成本和统计效率以及随附的统计方法的设计大多缺乏。该提案通过开发和评估成本效益的两相分层病例对照研究设计和统计推断方法来响应该PA，以开发和评估癌症的绝对风险预测模型。我们提出的工作建立在一种流行的方法上，该方法将病例对照数据和癌症和死亡率的外部危害率整合起来，以预测癌症的绝对风险。作为我们统计方法和随附软件的展示示例，我们探讨了预测乳腺癌风险的体积乳房密度的附加值。