Efficient estimation of the maximal association between multiple predictors and a survival outcome
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多个预测变量与生存结局最大关联的有效估计
- 研究问题
- 在高维右删失生存数据中，筛选预测变量后如何对最大线性关联进行有效推断，并兼顾选择偏差控制与计算可扩展性？
- 方法与思路
- 作者构造线性关联的半参数有效估计，建立检验任一预测变量关联的统计量，再用类似 bagging 的稳定化实现正态校准。
- 材料陈述的贡献
- 摘要称方法可构造最大关联的置信区间并降低计算成本；理论允许变量数超多项式增长，模拟和病毒基因表达应用提供展示。
- 阅读关注点
- 阅读全文时应核对最大关联的定义、删失与辅助估计条件，以及稳定化如何处理选择偏差，特别检查高维增长结果的限定条件。
- 阅读边界
- 仅据 OpenAlex 索引摘要，稿件版本未注明。具体理论条件、覆盖率和计算成本未提供；关联推断不能据此解释为因果效应或药物作用机制。
原文摘要
This paper develops a new approach to post-selection inference for screening high-dimensional predictors of survival outcomes. Post-selection inference for right-censored outcome data has been investigated in the literature, but much remains to be done to make the methods both reliable and computationally-scalable in high dimensions. Machine learning tools are commonly used to provide predictions of survival outcomes, but the estimated effect of a selected predictor suffers from confirmation bias unless the selection is taken into account. The new approach involves the construction of semiparametrically efficient estimators of the linear association between the predictors and the survival outcome, which are used to build a test statistic for detecting the presence of an association between any of the predictors and the outcome. Further, a stabilization technique reminiscent of bagging allows a normal calibration for the resulting test statistic, which enables the construction of confidence intervals for the maximal association between predictors and the outcome and also greatly reduces computational cost. Theoretical results show that this testing procedure is valid even when the number of predictors grows superpolynomially with sample size, and our simulations support this asymptotic guarantee at moderate sample sizes. The new approach is applied to the problem of identifying patterns in viral gene expression associated with the potency of an antiviral drug.