Post‐selection inference for the Cox model with interval‐censored data
针对区间删失数据下的Cox比例风险模型，提出了一种基于lasso模型选择后的推断方法，可给出渐近有效的p值和置信区间，并通过模拟和阿尔茨海默病研究验证了其性能。
We develop a post-selection inference method for the Cox proportional hazards model with interval-censored data, which provides asymptotically valid p-values and confidence intervals conditional on the model selected by lasso. The method is based on a pivotal quantity that is shown to converge to a uniform distribution under local parameters. Our method involves estimation of the efficient information matrix, for which several approaches are proposed with proof of their consistency. Thorough simulation studies show that our method has satisfactory performance in samples of modest sizes. The utility of the method is illustrated via an application to an Alzheimer's disease study.