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

项目摘要 由于癌症的人口负担，准确评估风险是肿瘤学的首要任务。乳腺癌 是全球女性中领先的癌症诊断，因此具有最长和最广泛的关注 风险预测。大多数传统的预测模型只利用已知与以下因素相关的基线因素： 乳腺癌风险。最近的模型扩展到更加强调基因组风险因素。然而，在这方面， 添加基因组风险标记的主要移动结合了对时间不变的测量（基于 SNPs），并不一定能解决改善乳腺癌风险分类的挑战。的 随时间变化的协变量部分反映了患者之间和患者内部随时间变化的内在异质性 轨迹，这可能为预测乳腺癌风险提供重要信息。积累 癌症风险的终身，乳腺癌有据可查，是理想的方法，纳入时间- 变协变量该提案的目的是提供新的统计模型， 以个性化的、动态的方式对患者异质性进行评估，从而产生更准确的风险预测方案。 所提出的算法包括创新的功能方法，以全面表征 改变模式的纵向轨迹由一组结果独立/无监督和结果- 依赖/监督特征。个体特异性特征集将包含关于所观察到的 时间变化的“模式”，而不是现有方法中的一次性曝光，导致更高的预测能力。 动态预测模型将以逐步的方式建立，从单个时变协变量开始， 并扩展到多变量设置，以适应多个随时间变化的协变量。拟议 方法将应用于护士健康研究，并在马约医院进行进一步的外部评估。 乳房X光检查健康研究。所有提出的方法都将伴随着用户友好的开源 软件

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.