带有内部验证失效指标交叉拟合估计、风险集线性化及验证设计的正交验证增强Cox回归
Orthogonal Validation-Augmented Cox Regression with Internally Validated Failure Indicators Cross-Fitted Estimation, Risk-Set Linearization, and Validation Design
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中文总结 AI 辅助
针对内部验证样本仅能提供部分金标准事件分类的问题，提出正交验证增强Cox（OVAC）回归，该方法可降低经验方差，实现双稳健识别与校准推断，为Cox估计提供机器学习兼容方案及验证设计指导。
中文摘要 AI 辅助
当仅内部验证样本可获得金标准事件分类时，会出现不完整判定和终点误分类问题。在随访时间和协变量准确但事件指标易出错的情况下，朴素Cox回归会产生偏差，而逆概率加权（IPW）效率低下。我们开发了正交验证增强Cox（OVAC）回归，这是一种针对不完整失效指标的交叉拟合增强Cox估计量。OVAC用交叉拟合增强伪事件替代缺失的事件指标，并明确保留估计Cox风险集均值的一阶贡献。该项通过验证机制对全数据影响函数进行投影得到，将得分展开与观测数据有效影响函数关联起来。该得分呈现精确的乘积形式干扰漂移，在乘积率条件下可实现双稳健识别、奈曼正交性及根n推断。在1000次R重复中，全方差标准误（SE）与标准差（SD）的比值分别为0.997和0.992，95%覆盖率为0.955和0.948。200次重复诊断显示，仅风险集项占全SE幅度的77.8%和81.1%，尽管协方差抵消使其净SE效应小于0.3%。在250次重复的方法比较中，OVAC相对于IPW将经验方差分别降低了38.8%和37.7%。OVAC提供了与机器学习兼容的Cox估计，具有明确的风险集线性化、校准推断及验证设计指导。
英文摘要
Incomplete adjudication and endpoint misclassification arise when gold-standard event classification is available only for an internal validation sample. With accurate follow-up times and covariates but error-prone event indicators, naive Cox regression can be biased and inverseprobability weighting inefficient. We develop orthogonal validation-augmented Cox (OVAC) regression, a cross-fitted augmented Cox estimator for incomplete failure indicators. OVAC replaces the missing event indicator with a cross-fitted augmented pseudo-event and explicitly retains the first-order contribution from estimating the Cox risk-set mean. The same term emerges from projection of the full-data influence function through the validation mechanism, linking the score expansion to the observed-data efficient influence function. The score exhibits exact product-form nuisance drift, yielding double-robust identification, Neyman orthogonality, and root-n inference under product-rate conditions. In 1,000 R replications, full-variance SE/SD ratios were 0.997 and 0.992, with 95% coverage of 0.955 and 0.948. A 200-replication diagnostic showed that the risk-set term alone was 77.8% and 81.1% of the full-SE magnitude, although covariance cancellation made its net SE effect smaller than 0.3%. In a 250-replication method comparison, OVAC reduced empirical variance by 38.8% and 37.7% relative to IPW. OVAC provides machine-learning-compatible Cox estimation with explicit risk-set linearization, calibrated inference, and validation-design guidance.
发表机构
- Michigan State University（密歇根州立大学）
机构由 AI 辅助整理，请以论文原文为准。
