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Comparative study of statistical methods for clustered ROC data: nonparametric methods and multiple outputation methods

Comparative study of statistical methods for clustered ROC data: nonparametric methods and... In clustered receiver operating characteristic (ROC) data each patient has several normal and abnormal observations. Within the same cluster, observations are naturally correlated. Several nonparametric methods have been proposed in the literature to handle clustered data structure, but their performances on simulated and real datasets have not been compared. Recently, a multiple outputation method has been considered for clustered data in areas other than diagnostic accuracy to account for within-cluster correlation. The multiple outputation method offers a resampling-based alternative for one sample clustered data with or without covariates, or for hypothesis testing in two sample clustered data. The method does not require a specific within-cluster correlation structure and yields a valid estimator while accounting for the within-cluster correlations. This paper contributes to the literature by introducing the multiple outputation method to the ROC setting, and empirically comparing the performance of these clustered ROC curve methods. The performance of these methods is also evaluated through two real examples. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Biostatistics & Epidemiology Taylor & Francis

Comparative study of statistical methods for clustered ROC data: nonparametric methods and multiple outputation methods

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Comparative study of statistical methods for clustered ROC data: nonparametric methods and multiple outputation methods

Abstract

In clustered receiver operating characteristic (ROC) data each patient has several normal and abnormal observations. Within the same cluster, observations are naturally correlated. Several nonparametric methods have been proposed in the literature to handle clustered data structure, but their performances on simulated and real datasets have not been compared. Recently, a multiple outputation method has been considered for clustered data in areas other than diagnostic accuracy to account for...
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Publisher
Taylor & Francis
Copyright
© 2021 International Biometric Society – Chinese Region
ISSN
2470-9379
eISSN
2470-9360
DOI
10.1080/24709360.2021.1880224
Publisher site
See Article on Publisher Site

Abstract

In clustered receiver operating characteristic (ROC) data each patient has several normal and abnormal observations. Within the same cluster, observations are naturally correlated. Several nonparametric methods have been proposed in the literature to handle clustered data structure, but their performances on simulated and real datasets have not been compared. Recently, a multiple outputation method has been considered for clustered data in areas other than diagnostic accuracy to account for within-cluster correlation. The multiple outputation method offers a resampling-based alternative for one sample clustered data with or without covariates, or for hypothesis testing in two sample clustered data. The method does not require a specific within-cluster correlation structure and yields a valid estimator while accounting for the within-cluster correlations. This paper contributes to the literature by introducing the multiple outputation method to the ROC setting, and empirically comparing the performance of these clustered ROC curve methods. The performance of these methods is also evaluated through two real examples.

Journal

Biostatistics & Epidemiology – Taylor & Francis

Published: Jul 3, 2021

Keywords: ROC curve; Wilcoxon statistics; diagnostic tests; empirical estimators; multiple outputation

References

  • Receiver operating characteristic (ROC) curves: review of methods with applications in diagnostic medicine
  • Comparing the areas under two or more correlated receiver operating characteristic curves: A nonparametric approach
  • Nonparametric analysis of clustered ROC curve data
  • A unified approach to nonparametric comparison of receiver operating characteristic curves for longitudinal and clustered data
  • Within-cluster resampling
  • Longitudinal data analysis for discrete and continuous outcomes
  • Multiple outputation: inference for complex clustered data by averaging analyses from independent data
  • Marginal analysis of measurement agreement among multiple raters with non-ignorable missing ratings
  • Multiple outputation for the analysis of longitudinal data subject to irregular observation
  • Incorporation of clustering effects for the Wilcoxon rank sum test: A large-sample approach
  • Rank-sum tests for clustered data