Nonparametric ROC summary statistics for correlated diagnostic marker data

Liansheng Larry Tang, Aiyi Liu, Zhen Chen, Enrique F. Schisterman, Bo Zhang, Zhuang Miao

Research output: Contribution to journalArticle

Abstract

We propose efficient nonparametric statistics to compare medical imaging modalities in multi-reader multi-test data and to compare markers in longitudinal ROC data. The proposed methods are based on the weighted area under the ROC curve, which includes the area under the curve and the partial area under the curve as special cases. The methods maximize the local power for detecting the difference between imaging modalities. We develop the asymptotic results of the proposed methods under a complex correlation structure. Our simulation studies show that the proposed statistics result in much better powers than existing statistics. We apply the proposed statistics to an endometriosis diagnosis study.

Original languageEnglish (US)
Pages (from-to)2209-2220
Number of pages12
JournalStatistics in Medicine
Volume32
Issue number13
DOIs
StatePublished - Jun 15 2013
Externally publishedYes

Keywords

  • Correlated data
  • Optimal weights
  • ROC curve
  • Wilcoxon statistics

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability

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  • Cite this

    Tang, L. L., Liu, A., Chen, Z., Schisterman, E. F., Zhang, B., & Miao, Z. (2013). Nonparametric ROC summary statistics for correlated diagnostic marker data. Statistics in Medicine, 32(13), 2209-2220. https://doi.org/10.1002/sim.5654