Direct estimation of the area under the receiver operating characteristic curve with verification biased data.
AUC
ROC curve
sensitivity
specificity
verification bias
Journal
Statistics in medicine
ISSN: 1097-0258
Titre abrégé: Stat Med
Pays: England
ID NLM: 8215016
Informations de publication
Date de publication:
30 12 2020
30 12 2020
Historique:
received:
08
07
2020
revised:
14
08
2020
accepted:
23
08
2020
pubmed:
19
9
2020
medline:
22
6
2021
entrez:
18
9
2020
Statut:
ppublish
Résumé
In medical diagnostic studies, verification of the true disease status might be partially missing based on results of diagnostic tests and other characteristics of subjects. Because estimates of area under the ROC curve (AUC) based on partially validated subjects are usually biased, it is usually necessary to estimate AUC from a bias-corrected ROC curve. In this article, various direct estimation methods of the AUC based on hybrid imputation [full imputations and mean score imputation (MSI)], inverse probability weighting, and the semiparametric efficient (SPE) approach are proposed and compared in the presence of verification bias when the test result is continuous under the assumption that the true disease status, if missing, is missing at random. Simulation results show that the proposed estimators are accurate for the biased sampling if the disease and verification models are correctly specified. The SPE and MSI based estimators perform well even under the misspecified disease/verification models. Numerical studies are performed to compare the finite sample performance of the proposed approaches with existing methods. A real dataset of neonatal hearing screening study is analyzed.
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
4789-4820Informations de copyright
© 2020 John Wiley & Sons Ltd.
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