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10.1002/sim.70327 http://scihub22266oqcxt.onion/10.1002/sim.70327 41351166!?!41351166       Stat+Med 2025 ; 44 (28-30): e70327 Nephropedia Template TP {{tp|p=41351166|t=2025. Likelihood-Based Non-Parametric Receiver Operating Characteristic Curve Analysis in the Presence of Imperfect Reference Standard |pdf=|usr=}}{{41351166}} gab.com Text Likelihood-Based Non-Parametric Receiver Operating Characteristic Curve Analysis in the Presence of Imperfect Reference Standard JO: Stat Med http://www.kidney.de/pget.php?&tw=1&pmid=41351166 #FOAvip In diagnostic studies, researchers frequently encounter imperfect reference standards with some misclassified labels. Treating these as gold standards can bias receiver operating characteristic (ROC) curve analysis. To address this issue, we propose a novel likelihood-based method under a non-parametric density ratio model. This approach enables the reliable estimation of the ROC curve, area under the curve (AUC), partial AUC, and Youden's index with favorable statistical properties. To implement the method, we develop an efficient expectation-maximization (EM) algorithm. Extensive simulations evaluate its finite-sample performance, showing smaller mean squared errors in estimating the ROC curve, partial AUC, and Youden's index compared to existing methods. We apply the proposed approach to a malaria study. Twit Text FOAVip Likelihood-Based Non-Parametric Receiver Operating Characteristic Curve Analysis in the Presence of Imperfect Reference Standard ????Stat Med http://www.kidney.de/pget.php?&tw=1&pmid=41351166 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=41351166 #FOAvip English Wikipedia <ref>{{Cite pmid|41351166}}</ref> Likelihood-Based Non-Parametric Receiver Operating Characteristic Curve Analysis in the Presence of Imperfect Reference Standard #MMPMID41351166 Sun Y; Sang P; Tian Q; Li P Stat Med 2025[Dec]; 44 (28-30): e70327 PMID41351166show ga In diagnostic studies, researchers frequently encounter imperfect reference standards with some misclassified labels. Treating these as gold standards can bias receiver operating characteristic (ROC) curve analysis. To address this issue, we propose a novel likelihood-based method under a non-parametric density ratio model. This approach enables the reliable estimation of the ROC curve, area under the curve (AUC), partial AUC, and Youden's index with favorable statistical properties. To implement the method, we develop an efficient expectation-maximization (EM) algorithm. Extensive simulations evaluate its finite-sample performance, showing smaller mean squared errors in estimating the ROC curve, partial AUC, and Youden's index compared to existing methods. We apply the proposed approach to a malaria study. |*ROC Curve[MESH] |Algorithms[MESH] |Area Under Curve[MESH] |Computer Simulation[MESH] |Humans[MESH] |Likelihood Functions[MESH] |Malaria/diagnosis[MESH] |Models, Statistical[MESH] |Reference Standards[MESH]Likelihood-Based Non-Parametric Receiver Operating Characteristic Curv(epmc).pdf DeepDyve Pubget Overpricing