  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.
