Testing the significance of crossover receiver operating characteristic curves in the presence of multiple markers

Background & Aim: In multivariate receiver operating characteristic (MROC) curve analysis, comparing two tests is usually done by means of area under the curve (AUC’s) and sensitivities. However, the existing procedures have not addressed the issue of comparing two MROC curves when they cross e...

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Main Authors: Sameera Govindaraju Govindaraju, Vishnu Vardhan Rudravaram
Format: Article
Language:English
Published: Tehran University of Medical Sciences 2017-10-01
Series:Journal of Biostatistics and Epidemiology
Subjects:
Online Access:https://jbe.tums.ac.ir/index.php/jbe/article/view/129
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spelling doaj-bce3e065e8f540d980e845b5430aa89d2020-12-06T04:15:21ZengTehran University of Medical SciencesJournal of Biostatistics and Epidemiology2383-41962383-420X2017-10-0124Testing the significance of crossover receiver operating characteristic curves in the presence of multiple markersSameera Govindaraju Govindaraju0Vishnu Vardhan Rudravaram1Department of Statistics, Ramanujan School of Mathematical Sciences, Pondicherry University, Puducherry, IndiaDepartment of Statistics, Ramanujan School of Mathematical Sciences, Pondicherry University, Puducherry, India Background & Aim: In multivariate receiver operating characteristic (MROC) curve analysis, comparing two tests is usually done by means of area under the curve (AUC’s) and sensitivities. However, the existing procedures have not addressed the issue of comparing two MROC curves when they cross each other. Methods & Materials: A modified version of AUC (mAUC) under MROC setup is proposed to address the above-mentioned problem. It is also shown that mAUC performs better than AUC. The performance of mAUC in the aspect of crossover curves is supported by a real dataset and simulation studies at different sample sizes. Results: Two real datasets, namely, Intra Uterine Growth Restricted Fetal Doppler Study (IUGRFDS) and Indian liver patient (ILP) datasets are used and apart from these simulation studies are also carried out to observe the effect of sample size. These mAUC’s are then compared with each other to show that difference exists between two curves while comparing AUC’s cannot identify the true difference existing between them. With respect to IUGRFDS dataset, MROC curves of the diagnostic procedures middle cerebral artery and cerebroplacental ratio cross each other and are found to be similar when their AUC’s and mAUC’s are compared. In ILP dataset, the extent of correct classification achieved in the case of males is shown to be better than that of females when mAUC’s at 0.5 and 0.8 are compared. Conclusion: It is observed that the mAUC’s are competent in identifying the true difference between the crossover MROC curves when the sample size is adequate, and the λ values are 0.5 and 0.8 but not 0.3. https://jbe.tums.ac.ir/index.php/jbe/article/view/129MultivariateArea under the curveCrossing-overMultivariate receiver operating characteristic curve
collection DOAJ
language English
format Article
sources DOAJ
author Sameera Govindaraju Govindaraju
Vishnu Vardhan Rudravaram
spellingShingle Sameera Govindaraju Govindaraju
Vishnu Vardhan Rudravaram
Testing the significance of crossover receiver operating characteristic curves in the presence of multiple markers
Journal of Biostatistics and Epidemiology
Multivariate
Area under the curve
Crossing-over
Multivariate receiver operating characteristic curve
author_facet Sameera Govindaraju Govindaraju
Vishnu Vardhan Rudravaram
author_sort Sameera Govindaraju Govindaraju
title Testing the significance of crossover receiver operating characteristic curves in the presence of multiple markers
title_short Testing the significance of crossover receiver operating characteristic curves in the presence of multiple markers
title_full Testing the significance of crossover receiver operating characteristic curves in the presence of multiple markers
title_fullStr Testing the significance of crossover receiver operating characteristic curves in the presence of multiple markers
title_full_unstemmed Testing the significance of crossover receiver operating characteristic curves in the presence of multiple markers
title_sort testing the significance of crossover receiver operating characteristic curves in the presence of multiple markers
publisher Tehran University of Medical Sciences
series Journal of Biostatistics and Epidemiology
issn 2383-4196
2383-420X
publishDate 2017-10-01
description Background & Aim: In multivariate receiver operating characteristic (MROC) curve analysis, comparing two tests is usually done by means of area under the curve (AUC’s) and sensitivities. However, the existing procedures have not addressed the issue of comparing two MROC curves when they cross each other. Methods & Materials: A modified version of AUC (mAUC) under MROC setup is proposed to address the above-mentioned problem. It is also shown that mAUC performs better than AUC. The performance of mAUC in the aspect of crossover curves is supported by a real dataset and simulation studies at different sample sizes. Results: Two real datasets, namely, Intra Uterine Growth Restricted Fetal Doppler Study (IUGRFDS) and Indian liver patient (ILP) datasets are used and apart from these simulation studies are also carried out to observe the effect of sample size. These mAUC’s are then compared with each other to show that difference exists between two curves while comparing AUC’s cannot identify the true difference existing between them. With respect to IUGRFDS dataset, MROC curves of the diagnostic procedures middle cerebral artery and cerebroplacental ratio cross each other and are found to be similar when their AUC’s and mAUC’s are compared. In ILP dataset, the extent of correct classification achieved in the case of males is shown to be better than that of females when mAUC’s at 0.5 and 0.8 are compared. Conclusion: It is observed that the mAUC’s are competent in identifying the true difference between the crossover MROC curves when the sample size is adequate, and the λ values are 0.5 and 0.8 but not 0.3.
topic Multivariate
Area under the curve
Crossing-over
Multivariate receiver operating characteristic curve
url https://jbe.tums.ac.ir/index.php/jbe/article/view/129
work_keys_str_mv AT sameeragovindarajugovindaraju testingthesignificanceofcrossoverreceiveroperatingcharacteristiccurvesinthepresenceofmultiplemarkers
AT vishnuvardhanrudravaram testingthesignificanceofcrossoverreceiveroperatingcharacteristiccurvesinthepresenceofmultiplemarkers
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