Nonparametric bootstrap methods for interval estimation of the area under the ROC curve for correlated diagnostic test data Pang, Jinji
dc.contributor.committeeMember Liu, Peng
dc.contributor.committeeMember Zhang, Qijing
dc.contributor.majorProfessor Wang, Chong 2023-01-19T15:25:29Z 2023-01-19T15:25:29Z 2022 2022-12
dc.description.abstract An essential part of contemporary diagnostic research is the development of new diagnostic assays and their performance evaluation. The receiver operating characteristic curve (ROC) and the area under the ROC curve (AUC) are frequently used in evaluating the performance of diagnostic assays. The variation in AUC estimation can be quantified non-parametrically using resampling methods, and then used to construct interval estimation for the AUC. For example, the pROC package is a widely used open-source tool that can calculate interval estimates of the AUC by performing nonparametric bootstrapping. However, when multiple observations are observed from the same subject, which is very common in diagnostic tests evaluation experiments, traditional bootstrap-based methods, such as the one in the pROC package, may fail to provide valid interval estimation of AUC. In particular, the traditional method does not account for the correlation among data observations and would result in interval estimation that does not cover the true AUC at the desired level of confidence. In this paper, we propose two novel methods to calculate the confidence interval of the AUC for correlated diagnostic test data, based on cluster bootstrapping and hierarchical bootstrapping, respectively. Our simulation studies showed that both proposed methods have higher coverage probabilities compared with the existing method when there are intra-subject correlations. We also discuss an application of the proposed methods to the evaluation of a novel whole-virus ELISA diagnostic essay in detection of porcine parainfluenza virus type-1 antibody in swine serum.
dc.language.iso en_US
dc.rights.holder Jinji Pang
dc.subject.disciplines DegreeDisciplines::Physical Sciences and Mathematics::Statistics and Probability
dc.subject.keywords ROC curve
dc.subject.keywords AUC
dc.subject.keywords nonparametric bootstrap methods
dc.subject.keywords correlated data analysis
dc.subject.keywords diagnostic test
dc.subject.keywords pROC package
dc.title Nonparametric bootstrap methods for interval estimation of the area under the ROC curve for correlated diagnostic test data
dc.type Text
dc.type.genre creativecomponent
dspace.entity.type Publication Statistics Statistics Masters Master of Science
Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
ISU-STAT-CC-Jinji Pang.docx
128.22 KB
Microsoft Word XML