{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T03:03:27Z","timestamp":1790046207059,"version":"4.0.1"},"reference-count":30,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T00:00:00Z","timestamp":1771459200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T00:00:00Z","timestamp":1771459200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Pharmaceutical Statistics"],"published-print":{"date-parts":[[2026,3]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>The advancement of precision medicine hinges on accurately tailored diagnostic strategies yet estimating reliable confidence intervals (CIs) for the maximal partial Youden Index under verification bias presents considerable challenges, especially within critical false positive rate (FPR) ranges (e.g., (0, 0.1), (0.05, 0.2)) vital for specific clinical applications. While previous work established the partial Youden Index framework, and methods like full imputation (FI), mean score imputation (MSI), inverse probability weighting (IPW), and semiparametric efficient (SPE) address verification bias, robustly integrating these for the partial index across demanding FPRs has needed further development. This paper significantly advances this area by adapting and applying these four bias\u2010correction techniques to estimate the partial Youden Index and its confidence interval (CIs) under verification bias. We systematically evaluate their performance with the proposed (bootstrap\u2010based, MOVER) CI construction approaches. Extensive simulations demonstrate distinct method\u2010specific patterns across verification proportions and FPR ranges, revealing the complexities in achieving reliable estimates. Bootstrap\u2010based CIs exhibit greater robustness to model misspecification, a common clinical uncertainty, while analytical CIs often face undercoverage issues. A cardiovascular disease biomarker analysis corroborates these findings, showing Blood Pressure's superior discriminatory capability compared to Pulse Rate. Operating under the Missing at Random (MAR) assumption, these results offer crucial, updated guidance for CI estimation in diagnostic studies with incomplete verification, providing significant value where precise evaluation in specific FPR regions is paramount and complete verification is unfeasible. Our findings enhance the statistical foundation for diagnostic test evaluation, extending beyond previous work by comprehensively addressing the partial Youden Index with these updated verification bias correction and CI formula applications.<\/jats:p>","DOI":"10.1002\/pst.70079","type":"journal-article","created":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T09:35:21Z","timestamp":1771493721000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Methodological Approaches for the Estimation of Confidence Intervals on Partial Youden Index Under Verification Bias"],"prefix":"10.1002","volume":"25","author":[{"given":"Sihan","family":"Jia","sequence":"first","affiliation":[{"name":"Department of Mathematics and Statistics Georgia State University  Atlanta Georgia USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shirui","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of 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