Comparison of ROC methods for partially-paired data

Brandon D. Gallas, Lorenzo L. Pesce

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

In this work we investigate ROC methods that compare the difference in AUCs (area under the ROC curve) from two modalities given partially paired data. Such methods are needed to accommodate the real world situations, where every case cannot be imaged or interpreted using both modalities. We compare variance estimation of the bivariate binormal-model based method ROCKIT of Metz et al., as well as several different non-parametric methods, including the bootstrap and U-statistics. This comparison explores different ROC curves, study designs (pairing structure of the data), sample sizes, case mix, and modality effect sizes.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2009
Subtitle of host publicationImage Perception, Observer Performance, and Technology Assessment
DOIs
StatePublished - 2009
Externally publishedYes
EventMedical Imaging 2009: Image Perception, Observer Performance, and Technology Assessment - Lake Buena Vista, FL, United States
Duration: Feb 11 2009Feb 12 2009

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume7263
ISSN (Print)1605-7422

Conference

ConferenceMedical Imaging 2009: Image Perception, Observer Performance, and Technology Assessment
Country/TerritoryUnited States
CityLake Buena Vista, FL
Period2/11/092/12/09

Keywords

  • AUC
  • Bootstrap
  • Missing data
  • Partially-paired data
  • ROCKIT
  • Receiver operating characteristic (ROC)

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Atomic and Molecular Physics, and Optics
  • Biomaterials
  • Radiology Nuclear Medicine and imaging

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