Dynamic prediction incorporating time-varying covariates for the onset of breast cancer

结合时变协变量的乳腺癌发病动态预测

• 批准号: 10296519
• 负责人: Shu Jiang
• 金额: $ 36.03万
• 依托单位: WASHINGTON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-01 至 2025-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10296519
• 关键词: Algorithms; Area; Breast Cancer Risk Factor; Cancer Burden; Cancer Control; Chronic; Chronic Kidney Failure; Classification; Code; Computer software; Coronary heart disease; Data; Disease; Evaluation; Eye diseases; Future; Genomics; Health; Heterogeneity; Hormone use; Individual; Life; Life Cycle Stages; Lifting; Mammography; Measures; Methodology; Methods; Modeling; Nurses' Health Study; Obesity; Oncology; Outcome; Patients; Pattern; Performance; Population; Population Sciences; Positioning Attribute; Postmenopause; Premenopause; Prevention; Principal Component Analysis; Prognosis; ROC Curve; Risk; Risk Assessment; Risk Factors; Risk Marker; Scheme; Statistical Models; Supervision; Time; Update; Validation; Woman; Work; base; cancer diagnosis; cancer risk; improved; innovation; malignant breast neoplasm; novel; open source; personalized risk prediction; precision oncology; predictive modeling; risk prediction; risk prediction model; simulation; survivorship; tool; trend; user-friendly

PROJECT SUMMARY Accurate assessment of risk is a top priority in oncology due to the population burden of cancer. Breast cancer is the leading cancer diagnosis among women worldwide and accordingly has the longest and broadest focus on risk prediction. Most traditional prediction models only utilize baseline factors known to be associated with breast cancer risk. More recent models expand to place greater emphasis on genomic risk factors. However, the predominant move of adding genomic risk markers incorporates a measure that is invariant to time (based on SNPs) and do not necessarily solve the challenge of improving breast cancer risk classification. The intrinsic heterogeneity between and within patients over time are reflected in part, by the time-varying covariate trajectories, which may provide important information for the prediction of breast cancer risk. The accumulation of cancer risk over life, well documented for breast cancer, is ideally suited to methods that incorporate time- varying covariates. Theobjective of this proposal is toprovide novel statistical models that can incorporate patient heterogeneity in a personalized, dynamic manner leading to a more accurate risk prediction scheme. The proposed algorithms encompass innovative functional approaches to comprehensively characterize the changing pattern of the longitudinal trajectories by a set of outcome-independent/unsupervised and outcome- dependent/supervised features. The set of individual-specific features will contain information on the observed time-varying `pattern' rather than one-time exposure in existing methods, leading to a higher predictive power. The dynamic prediction models will be built in a stepwise fashion, starting with a single time-varying covariate, and extended to the multivariate settings, to accommodate multiple time-varying covariates. The proposed methods will be applied to the Nurses' Health Study and further assessed externally in the Mayo Mammography Health Study. All of the proposed methods will be accompanied with user-friendly open-source software.

项目摘要 由于癌症的人口负担，准确评估风险是肿瘤学的重中之重。乳腺癌 是全球女性的领先癌症诊断，因此具有最长和最广泛的焦点 在风险预测上。大多数传统预测模型仅利用已知与之相关的基线因素 乳腺癌风险。最近的模型扩展到更加重视基因组风险因素。然而， 添加基因组风险标志物的主要举动包括时间不变的度量（基于 在SNP上），不一定要解决改善乳腺癌风险分类的挑战。这 随着时间的推移，患者之间和内部的内在异质性部分反映了随着时变的协变量 轨迹，这可能为预测乳腺癌风险提供重要信息。积累 对乳腺癌有充分记录的癌症风险，非常适合结合时间的方法 变化的协变量。该提案的主题是可培养的新颖统计模型，可以合并 以个性化的，动态的方式，患者异质性，导致更准确的风险预测方案。 所提出的算法涵盖了创新的功能方法，以全面表征 通过一组与结果无关/无监督和结果 - 依赖/监督功能。一组个人特定功能将包含有关观察到的信息 随时间变化的“模式”，而不是现有方法中的一次性暴露，从而导致更高的预测能力。 动态预测模型将以逐步的方式构建，从一个随时间变化的协变量开始， 并扩展到多元设置，以适应多个时间变化的协变量。提议 方法将应用于护士的健康研究，并在蛋黄酱中进一步评估 乳腺X线摄影健康研究。所有建议的方法都将伴随用户友好的开源 软件。