The area under the ROC curve (AUC) is the standard measure of a biomarker's discriminatory accuracy; however, naive AUC estimates can be misleading when validation cohorts differ from the intended target population. Such covariate shifts commonly arise under biased or non-random sampling, distorting AUC estimations and thus impeding both generalization and cross-study comparison of AUC. We develop an estimand-focused framework for valid AUC estimation and benchmarking under covariate shift. Leveraging balancing ideas from causal inference, we extend calibration weighting to the U-statistic framework for AUC estimation and introduce a family of estimators that accommodate both summary-level and patient-level information; in certain specifications, some of these estimators attain double robustness. Furthermore, we establish asymptotic properties and study their performances across a spectrum of covariate shift severities and calibration choices in comprehensive simulations. Finally, we demonstrate practical utility in the POWER trials by evaluating how baseline stair-climb power (SCP) predicts 6-month survival among advanced non-small-cell lung cancer (NSCLC) patients. Together, the results provide a principled toolkit for anchoring biomarker AUCs to clinically relevant target populations and for comparing them fairly across studies despite distributional differences.

翻译：ROC曲线下面积（AUC）是衡量生物标志物判别准确性的标准指标；然而，当验证队列与预期目标总体存在差异时，简单的AUC估计可能产生误导。此类协变量偏移通常源于有偏或非随机抽样，会扭曲AUC估计，从而阻碍AUC的泛化与跨研究比较。我们开发了一个基于估计目标的框架，用于在协变量偏移下进行有效的AUC估计与基准测试。借鉴因果推断中的平衡思想，我们将校准加权扩展至AUC估计的U统计量框架，并引入一系列可同时容纳汇总层面与患者层面信息的估计器；在某些设定下，部分估计器具备双重稳健性。此外，我们建立了渐近性质，并通过综合模拟研究了这些估计器在不同协变量偏移严重程度与校准选择下的表现。最后，我们通过评估基线爬楼梯功率（SCP）对晚期非小细胞肺癌（NSCLC）患者6个月生存期的预测能力，在POWER试验中展示了该方法的实用价值。综上，本研究为将生物标志物AUC锚定至临床相关目标总体，并在存在分布差异时实现跨研究的公平比较，提供了一套原则性工具集。