Literature DB >> 9629639

Comparing correlated areas under the ROC curves of two diagnostic tests in the presence of verification bias.

X H Zhou1.   

Abstract

To assess relative accuracies of two diagnostic tests, we often compare the areas under the receiver operating characteristic (ROC) curves of these two tests in a paired design. Standard methods for analyzing data from a paired design require that every patient tested has the known disease status. In practice, however, some of the patients with test results may not have verified disease status. Any analysis using only verified cases may result in verification bias. In this paper, we propose a verification bias correction procedure for comparing areas under ROC curves under the missing-at-random (MAR) assumption. We also develop an approach for testing the validity of the MAR assumption. Finally, we use the proposed method to compare the relative accuracies of MRI and CT in evaluation of pancreatic cancer.

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Year:  1998        PMID: 9629639

Source DB:  PubMed          Journal:  Biometrics        ISSN: 0006-341X            Impact factor:   2.571


  8 in total

1.  Semiparametric estimation of the covariate-specific ROC curve in presence of ignorable verification bias.

Authors:  Danping Liu; Xiao-Hua Zhou
Journal:  Biometrics       Date:  2011-03-01       Impact factor: 2.571

2.  Psychosis risk screening in different populations using the Prodromal Questionnaire: A systematic review.

Authors:  Mark Savill; Jennifer D'Ambrosio; Tyrone D Cannon; Rachel L Loewy
Journal:  Early Interv Psychiatry       Date:  2017-08-06       Impact factor: 2.732

3.  A model for adjusting for nonignorable verification bias in estimation of the ROC curve and its area with likelihood-based approach.

Authors:  Danping Liu; Xiao-Hua Zhou
Journal:  Biometrics       Date:  2010-12       Impact factor: 2.571

4.  Estimation of the ROC curve under verification bias.

Authors:  Ronen Fluss; Benjamin Reiser; David Faraggi; Andrea Rotnitzky
Journal:  Biom J       Date:  2009-06       Impact factor: 2.207

5.  Estimation of the disease-specific diagnostic marker distribution under verification bias.

Authors:  John H Page; Andrea Rotnitzky
Journal:  Comput Stat Data Anal       Date:  2009-01-15       Impact factor: 1.681

6.  New risk score of the early period after spontaneous subarachnoid hemorrhage: For the prediction of delayed cerebral ischemia.

Authors:  Yuan-Jian Fang; Shu-Hao Mei; Jia-Nan Lu; Yi-Ke Chen; Zhao-Hui Chai; Xiao Dong; Camila Araujo; Cesar Reis; Jian-Min Zhang; Sheng Chen
Journal:  CNS Neurosci Ther       Date:  2019-08-12       Impact factor: 5.243

7.  Comparison of aneurysmal subarachnoid hemorrhage grading scores in patients with aneurysm clipping and coiling.

Authors:  Yuanjian Fang; Jianan Lu; Jingwei Zheng; Haijian Wu; Camila Araujo; Cesar Reis; Cameron Lenahan; Suijun Zhu; Sheng Chen; Jianmin Zhang
Journal:  Sci Rep       Date:  2020-06-08       Impact factor: 4.379

8.  Validation and Comparison of Aneurysmal Subarachnoid Hemorrhage Grading Scales in Angiogram-Negative Subarachnoid Hemorrhage Patients.

Authors:  Yuanjian Fang; Shenbin Xu; Jianan Lu; Haijian Wu; Jingwei Zheng; Cameron Lenahan; Yang Cao; Sheng Chen; Zefeng Wang; Jianmin Zhang
Journal:  Biomed Res Int       Date:  2020-02-28       Impact factor: 3.411

  8 in total

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