Nonparametric bootstrap methods for interval estimation of the area under the ROC curve with correlated diagnostic test data: application to whole-virus ELISA testing in swine
Nonparametric bootstrap methods for interval estimation of the area under the ROC curve with correlated diagnostic test data: application to whole-virus ELISA testing in swine
Developing and evaluating novel diagnostic assays are crucial components of contemporary diagnostic research. The receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC) are frequently used to evaluate diagnostic assays’ performance. The variation in AUC estimation can be quantified nonparametrically using resampling methods, such as bootstrapping, and then used to construct interval estimation for the AUC. When multiple observations are observed from the same subject, which is very common in veterinary diagnostic tests evaluation experiments, a traditional bootstrap-based method can fail to provide valid interval estimations of AUC. In particular, the traditional method does not account for the correlation among data observations and could result in interval estimation that fails to cover the true AUC adequately at the desired confidence level. In this paper, we proposed 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 had adequate coverage probabilities which were higher than the existing traditional method when there were intra-subject correlations. We also discussed applying the proposed methods to evaluate a novel whole-virus ELISA (wv-ELISA) diagnostic assay in detecting porcine parainfluenza virus type-1 antibodies in swine serum.