Post-Selection Inference for Survival Outcomes in Precision Medicine

精准医学中生存结果的选择后推断

• 批准号: 2112938
• 负责人: Min Qian
• 金额: $ 25万
• 依托单位: Columbia University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2112938&HistoricalAwards=false
• 关键词: Post; Selection; Inference; Survival; Outcomes

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.

生物医学技术的最新突破对个别患者产生了大量数据。但是，通常，只有相对较少的特征（如果有的话）可以预测临床结果，尤其是在结果是生存时间时。在这种情况下，科学发现的一个主要方面是检测大量协变量中的重要预测因子。此外，在观察治疗分配的研究中，一个基本目标是制定精确医学的策略。为了实现这一目标，必须识别与治疗相互作用的协变量。由于最终的模型在为临床决策和指导未来的研究中发挥作用，因此为所选协变量提供推理保证至关重要。简而言之，这是选择后的推理问题。该项目的总体目标是开发一种统一的假设检验程序，该程序可用于检测可预测右审查下生存结果的变量，并确定可用于制定最佳治疗决策的生存结果的大量治疗相互作用。 该项目将开发新的选择后推断方法，以筛选生存结果的高维预测指标，并使用这些方法设计新的治疗选择策略。这个问题是具有挑战性的，不仅是因为未规则的渐近行为（测试统计和估计器的），而且还因为审查的存在。该计划涉及构建与斜率参数的半参数估计器（在加速故障时间模型中），该估计与每个预测变量与存活率之间的最大边缘相关性相对应，并设计了该统计量的正则化版本的校准，以提供正式的筛选测试，以提供大量关联。此外，将开发基于检测到的关联的最佳治疗政策的有效性的方法。预计所得的程序将比现有方法更强大，更有效。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的评论标准来评估值得支持的。

项目成果

Noncommutative Probability and Multiplicative Cascades

非交换概率和乘法级联

• DOI: 10.1214/20-sts780
• 发表时间: 2021
• 期刊: Statistical Science
• 影响因子: 5.7
• 作者: McKeague, Ian W.

Empirical Likelihood-Based Inference for Functional Means with Application to Wearable Device Data

基于经验似然的函数方法推理及其在可穿戴设备数据中的应用

• DOI: 10.1111/rssb.12543
• 发表时间: 2022
• 期刊: Journal of the Royal Statistical Society Series B: Statistical Methodology
• 作者: Chang, Hsin-wen;McKeague, Ian W.
• 通讯作者: McKeague, Ian W.

Fitting additive risk models using auxiliary information

• DOI: 10.1002/sim.9649
• 发表时间: 2023-01
• 期刊: Statistics in Medicine
• 影响因子: 2
• 作者: Jie Ding;Jialiang Li;Yang Han;I. McKeague;Xiaoguang Wang

Generalization error bounds of dynamic treatment regimes in penalized regression-based learning

• DOI: 10.1214/22-aos2171
• 发表时间: 2022-08
• 期刊: The Annals of Statistics
• 作者: E. J. Oh;Min Qian;Y. Cheung

A Case Study of Non-inferiority Testing with Survival Outcomes

生存结果的非劣效性检验案例研究

• 发表时间: 2021
• 期刊: Case studies in business industry and government statistics
• 作者: Chang, Hsin-wen;McKeague, Ian W.;Wang, Yu-Ju
• 通讯作者: Wang, Yu-Ju