通过校准汇总统计量对具有人工智能提取协变量的偏差校正Cox回归
Bias-corrected Cox regression with AI-extracted covariates via calibration summary statistics
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中文总结 AI 辅助
研究针对协变量受AI提取误差影响的情况，为Cox比例风险模型开发偏差校正框架，通过多变量校准框架分解偏差，给出校正估计器、置信区间及诊断方法，经合成数据实验验证可有效减少偏差并实现接近名义覆盖。
中文摘要 AI 辅助
大规模观察性研究越来越依赖人工智能管道从非结构化临床记录中提取结构化变量。常见工作流程将用金标准样本验证提取准确性的数据供应商与仅接收提取数据集和汇总准确性统计信息的下游研究人员分开。当协变量存在人工智能提取误差时，我们为Cox比例风险模型开发了一个偏差校正框架。在统一的多变量校准框架中，我们表明朴素Cox估计器的偏差分解为一个主阶校准项和一个随着提取准确性提高而消失的二阶残差。主阶项产生一个校正估计器，它作为对任何标准Cox软件输出的事后矩阵乘法。我们进一步推导了纳入校准不确定性的偏差调整置信区间和用于评估被忽略的残差是否会对推断产生重大影响的敏感性诊断。具有交叉相关提取误差和受控非线性校准违规的合成数据实验证实，即使在轻度违反线性校准假设的情况下，校正也能大幅减少偏差并实现接近名义覆盖。该框架产生了一个具体的报告规范：数据供应商应在生存分析中使用的任何人工智能提取的协变量数据集旁边提供的简短汇总统计量列表。
英文摘要
Large-scale observational studies increasingly rely on AI pipelines to extract structured variables from unstructured clinical records. A common workflow separates the data vendor, who validates extraction accuracy with a gold-standard sample, from the downstream researcher, who receives only the extracted dataset and summary accuracy statistics. We develop a bias-correction framework for the Cox proportional hazards model when covariates are subject to AI extraction error. Within a unified multivariate calibration framework, we show that the naive Cox estimator's bias decomposes into a leading-order calibration term and a second-order residual that vanishes as extraction accuracy improves. The leading-order term yields a corrected estimator that operates as a post-hoc matrix multiplication on the output of any standard Cox software. We further derive bias-adjusted confidence intervals that incorporate calibration uncertainty and a sensitivity diagnostic for assessing whether the neglected residual could materially affect inference. Synthetic data experiments with cross-dependent extraction errors and controlled nonlinear calibration violations confirm that the correction substantially reduces bias and achieves near-nominal coverage even under mild violations of the linear calibration assumption. The framework yields a concrete reporting specification: a short list of summary statistics that data vendors should provide alongside any AI-extracted covariate dataset used in survival analysis.
