Statistical Methods for Cancer Absolute Risk Prediction

描述(由申请人提供)：美国国家癌症研究所(NCI)发布的癌症风险和预后预测模型的开发、应用和评估支持创新统计方法的研究，以开发和评估新的和现有的癌症风险预测模型。预测的准确性是决定癌症风险预测模型在识别高危人群以进行早期预防的临床适用性的关键因素。随着癌症危险因素的不断涌现，迫切需要及时评估它们的附加值以进行预测。但研究设计大多缺乏成本与统计效率的平衡，缺乏相应的统计方法。这项建议通过开发和评估成本效益高的两阶段分层病例对照研究设计和统计推断方法来开发和评估癌症的绝对风险预测模型来回应这一PA。我们建议的工作建立在一种流行的方法上，该方法整合了病例对照数据和癌症和死亡率的外部危险率，以预测癌症的绝对风险。作为我们的统计方法和配套软件的展示示例，我们探索了体积乳房密度对预测乳腺癌风险的附加值。

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.