Impact of Molecular Biomarkers on Survival Outcomes: Signal Detection, Model Selection, and Statistical Inference in High-dimensional Settings

该项目的目标是提供一个理论上合理和计算上可行的框架，用于分析从流行病学和其他生物医学科学、社会科学、市场营销、环境科学和计量经济学收集的大规模数据。所提出的方法将提供一种方便的方法来确定基因特异性对生存的影响，并量化高维环境下估计的不确定性。因此，这些方法将有助于检测患者的特定特征，这些特征使他们对治疗反应不同或更容易患病，这是精准医疗的关键。PI还计划通过重新设计研究生课程和培养研究生来传播拟议的教育研究。通过三个主要目标，该项目采用了一系列方法来解决高维生存数据分析产生的基本问题。第一个目标是检测基因对癌症患者生存的特定影响，并提供一个综合框架，在基因具有异质影响模式时检测单个基因与生存的相关性。第二个目标侧重于依次选择预测生存结果的重要预测因子。该方法与现有的前向回归方法不同，前向回归方法不适用于处理具有高维协变量的删节结果数据。第三个目标是通过整合模型选择、估计和推理步骤，开发方法来量化具有高维预测因子的生存模型的不确定性。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The goal of this project is to provide a theoretically justifiable and computationally feasible framework for analyzing large-scale data collected from epidemiology and other biomedical sciences, social science, marketing, environmental science, and econometrics. The proposed methods will provid a convenient means to identify the gene-specific impacts on survival and quantify the uncertainty of the estimates in high-dimensional settings. As a result, the methods will help detect patients' specific characteristics that make them respond differently to treatment or more susceptible to diseases, which is key to precision medicine. The PI further plans to disseminate the proposed research in education by redesigning graduate-level courses and training graduate students. The project, through its three major aims, features a series of methods to address fundamental issues arising from high-dimensional survival data analysis. The first aim is to detect gene-specific impacts on cancer patients' survival and to provide an integrated framework of detecting individual genes' relevance to survival when genes have heterogeneous patterns of influence. The second aim focuses on sequentially selecting important predictors for predicting survival outcomes. This approach is different from existing forward regression approaches, which are not applicable to handle censored outcome data with high-dimensional covariates. The third aim is to develop methods to quantify the uncertainty of survival models with high-dimensional predictors by integrating model selection, estimation, and inference steps.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.