An Estimand-Focused Approach for AUC Estimation, Generalization, and Comparison: From Non-representative Samples to Target Population
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866912718451113984 |
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| author | Liu, Jiajun Mao, Guangcai Wang, Xiaofei |
| author_facet | Liu, Jiajun Mao, Guangcai Wang, Xiaofei |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_14992 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | An Estimand-Focused Approach for AUC Estimation, Generalization, and Comparison: From Non-representative Samples to Target Population Liu, Jiajun Mao, Guangcai Wang, Xiaofei Methodology 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. |
| title | An Estimand-Focused Approach for AUC Estimation, Generalization, and Comparison: From Non-representative Samples to Target Population |
| topic | Methodology |
| url | https://arxiv.org/abs/2511.14992 |