标题： 高维生存分析：方法与应用 显示英文标题

标题： High-Dimensional Survival Analysis: Methods and Applications

摘要： 在精准医学时代，诸如死亡或进展的时间等时间-事件结果通常与高通量协变量一起被收集。 这些高维数据使得经典生存回归模型难以拟合，或者由于过拟合可能导致预测能力低下。 为克服这一问题，最近的研究重点放在开发用于特征选择和生存预后的新方法上。 我们将回顾处理高维预测变量的生存结果数据的各种前沿方法，并突出机器学习方法在生存预测方面的最新创新。 我们将涵盖这些方法背后的统计直觉和原理，并以存在竞争事件的情况作为更复杂设置的扩展来结束。 我们通过波士顿肺癌生存队列研究的应用来说明这些方法，这是研究肺癌复杂机制的最大癌症流行病学队列之一。 显示英文摘要

摘要： In the era of precision medicine, time-to-event outcomes such as time to death or progression are routinely collected, along with high-throughput covariates. These high-dimensional data defy classical survival regression models, which are either infeasible to fit or likely to incur low predictability due to over-fitting. To overcome this, recent emphasis has been placed on developing novel approaches for feature selection and survival prognostication. We will review various cutting-edge methods that handle survival outcome data with high-dimensional predictors, highlighting recent innovations in machine learning approaches for survival prediction. We will cover the statistical intuitions and principles behind these methods and conclude with extensions to more complex settings, where competing events are observed. We exemplify these methods with applications to the Boston Lung Cancer Survival Cohort study, one of the largest cancer epidemiology cohorts investigating the complex mechanisms of lung cancer.