Comparison of Paired ROC Curves through a Two-Stage Test

Yu, Wenbao; Park, Eunsik; Chang, Yuan‐chin Ivan

doi:10.1080/10543406.2014.920874

Cited by 4 publications

(3 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…By the comparison of areas under the two ROC curves, we can estimate which one of two diagnostic tests is more suitable for discriminating nondiseased subjects from diseased subjects or any other two conditions of interest. 7 Braun & Alonzo 11 proposed a modified rank test that does not require such a transformation and showed that the modified test has the same power as Bandos et al, 6 Bantis & Feng., 12 focused on comparing two correlated ROC curves at a given specificity level. They proposed parametric approaches, transformations to normality, and nonparametric kernel-based approaches.…”

Section: ∑∑mentioning

confidence: 99%

An extended McNemar test for comparing correlated proportion of positive responses

Marius¹,

Happiness²

2019

BBIJ

View full text Add to dashboard Cite

When comparing two diagnostic procedures, the difference between AUCs is often used and to control for the sources of changes arising from changes due to subjects which represents a reasonable size of the overall changes of the AUC, a paired data is recommended. This is because paired data usually induces positive correlation between the test results of the same subjects. Based on the use of paired data, Sumi et al., 7 adopted the usual McNemar 8 test for comparing two correlated marginal probability of positive responses in diagnostic test procedures. This paper is an extension of this work for evaluating the performance of two diagnostic tests in terms of the proportion of positive responses and the comparison of this method with the existing tests by DeLong et al., 3 Bandos et al., 6 Sumi et al. 7 Estimation of AUC In estimating the AUC, two main factors have to be considered namely, the design of the study and the distribution of test result. 9 Under the study design, test results or dataset can be classified into three types namely: (i) paired data (ii) unpaired data and (iii) partially paired data. For the paired and partially-paired set of data, correlation between AUCs is considered. Under the distribution type of test

show abstract

Section: ∑∑mentioning

confidence: 99%

An extended McNemar test for comparing correlated proportion of positive responses

Marius¹,

Happiness²

2019

BBIJ

View full text Add to dashboard Cite

show abstract

“…Based on Yu et al (2014Yu et al ( , 2015, a heuristic choice of δ is z 1−α/2 σ k , where z α is the α quantile of a standard normal distribution, and σ k is the standard deviation of X X X k . From Equation (2.2), λ determines how much weight is put on the original AUC.…”

Section: Mauc For Gene Rankingmentioning

confidence: 99%

“…In this work, we fix δ and vary λ for comparison. More details on mAUC can be found in Yu et al (2014Yu et al ( , 2015.…”

Section: Mauc For Gene Rankingmentioning

confidence: 99%

Applying a modified AUC to gene ranking

Chang

Park

2018

CSAM

Self Cite

View full text Add to dashboard Cite

High-throughput technologies enable the simultaneous evaluation of thousands of genes that could discriminate different subclasses of complex diseases. Ranking genes according to differential expression is an important screening step for follow-up analysis. Many statistical measures have been proposed for this purpose. A good ranked list should provide a stable rank (at least for top-ranked gene), and the top ranked genes should have a high power in differentiating different disease status. However, there is a lack of emphasis in the literature on ranking genes based on these two criteria simultaneously. To achieve the above two criteria simultaneously, we proposed to apply a previously reported metric, the modified area under the receiver operating characteristic cure, to gene ranking. The proposed ranking method is found to be promising in leading to a stable ranking list and good prediction performances of top ranked genes. The findings are illustrated through studies on both synthesized data and real microarray gene expression data. The proposed method is recommended for ranking genes or other biomarkers for high-dimensional omics studies.

show abstract

Performance of tests based on the area under the ROC curve for multireader diagnostic data

Hwang,

Hsu,

2024

Journal of Applied Statistics

View full text Add to dashboard Cite

Comparison of Paired ROC Curves through a Two-Stage Test

Cited by 4 publications

References 22 publications

An extended McNemar test for comparing correlated proportion of positive responses

An extended McNemar test for comparing correlated proportion of positive responses

Applying a modified AUC to gene ranking

Performance of tests based on the area under the ROC curve for multireader diagnostic data

Contact Info

Product

Resources

About