Post-Selection Inference for Survival Outcomes in Precision Medicine

生物医学技术的最新突破产生了大量的个体患者数据。然而，通常情况下，只有相对较少的特征（如果有的话）可以预测临床结果，特别是当结果是生存时间时。在这种情况下，科学发现的一个核心方面是在大量协变量中发现重要的预测因子。此外，在观察治疗分配的研究中，一个基本目标是制定精准医疗策略。为了实现这一目标，确定与治疗相互作用的协变量是很重要的。由于所得到的模型拟合在为临床决策提供信息和指导未来的研究中发挥作用，因此为选定的协变量提供推断保证至关重要。简而言之，这就是选择后推理问题。该项目的总体目标是开发一个统一的假设检验程序，该程序可用于检测在正确审查下预测生存结果的变量，以及确定生存结果的显著协变量相互作用，可用于制定最佳治疗决策。该项目将开发筛选生存结果的高维预测因子的选择后推断的新方法，并使用这些方法来设计新的治疗选择政策类别。这个问题是具有挑战性的，不仅因为（检验统计量和估计量的）不规则渐近行为，而且因为审查的存在。该计划包括构建斜率参数的半参数有效估计器（在加速失效时间模型中），该估计器对应于每个预测器与生存结果之间的最大边际相关性，并设计该统计量的正则化版本的校准，以提供将检测显着关联的正式筛选测试。此外，基于检测到的关联，将开发构建和评估最佳治疗政策有效性的方法。由此产生的程序预计将比现有方法更强大、更有效。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。

Recent breakthroughs in biomedical technology produce massive amounts of data on individual patients. Typically, however, only a relatively small number of features, if any, may be predictive of the clinical outcome, especially when the outcome is a survival time. A central aspect of scientific discovery in this scenario is to detect significant predictors among a large set of covariates. In addition, in studies where treatment assignments are observed, an essential goal is to develop strategies for precision medicine. To achieve this goal, it is important to identify covariates that interact with the treatment. As the resulting model fits play a role in informing clinical decisions and guiding future research, it is crucial to provide inferential guarantees for the selected covariates. This is the post-selection inference problem in a nutshell. The overarching goal of this project is to develop a unified hypothesis testing procedure that can be used to detect variables that are predictive of survival outcomes under right censoring, as well as to identify significant treatment-by-covariate interactions of survival outcomes that can be used in making optimal treatment decisions. This project will develop new methods of post-selection inference for screening high-dimensional predictors of survival outcomes and use those methods to design new classes of treatment selection policies. The problem is challenging, not only because of nonregular asymptotic behavior (of test statistics and estimators), but also because of the presence of censoring. The plan involves construction of a semi-parametrically efficient estimator of the slope parameter (in an accelerated failure time model) corresponding to the maximal marginal correlation between each predictor and the survival outcome, and devising a calibration of a regularized version of this statistic to furnish a formal screening test that will detect significant associations. Further, methods of constructing and assessing the effectiveness of optimal treatment policies based on the detected associations will be developed. The resulting procedures are expected to be more powerful and efficient than existing methods.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.