变量选择后如何准确推断生存模型系数
Statistical inference after variable selection in Cox models: A simulation study
- 用样本分割、去偏Lasso等方法修正选变量后的统计偏差
- 在真实生物医学数据结构下，去偏Lasso表现更稳定可靠
- 适合做生存分析中变量选择后的严谨推断研究者
选择相关预测变量是生物医学生存数据建模的核心。传统频率推断假设协变量集预先固定，未考虑数据驱动的变量选择，导致事后推断可能产生偏差和误导。在右删失生存数据中，删失带来的额外不确定性使问题更为严重。本文研究了在Cox模型中对Lasso及其扩展自适应Lasso的回归系数，在变量选择后采用多种推断方法的表现，包括样本分割、精确事后推断和去偏Lasso。通过模拟研究评估其性能，模拟设定反映生物医学中常见的协变量结构和删失率。为补充模拟结果，还基于公开生存数据集展示了这些方法的实际应用效果。
原文摘要 · Abstract (English)
Choosing relevant predictors is central to the analysis of biomedical time-to-event data. Classical frequentist inference, however, presumes that the set of covariates is fixed in advance and does not account for data-driven variable selection. As a consequence, naive post-selection inference may be biased and misleading. In right-censored survival settings, these issues may be further exacerbated by the additional uncertainty induced by censoring. We investigate several inference procedures applied after variable selection for the coefficients of the Lasso and its extension, the adaptive Lasso, in the context of the Cox model. The methods considered include sample splitting, exact post-selection inference, and the debiased Lasso. Their performance is examined in a neutral simulation study reflecting realistic covariate structures and censoring rates commonly encountered in biomedical applications. To complement the simulation results, we illustrate the practical behavior of these procedures in an applied example using a publicly available survival dataset.
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