Literature DB >> 18646265

Nonparametric statistical inference method for partial areas under receiver operating characteristic curves, with application to genomic studies.

Yaohua He1, Michael Escobar.   

Abstract

Recently ROC50 index-the area under the lower portion of the receiver operating characteristic (ROC) curve up to the first 50 false positives-has been increasingly widely used in genomic research. Unfortunately, statistical inferences on the ROC50 index are not commonly drawn due to a lack of handy statistical inference methods and/or software tools. In this paper, we reviewed developments in statistical methods for the partial areas under ROC curves and using nonparametric methods we derived a simple and direct variance calculation formula for the partial areas, different from existing methods in the literature. We have also verified our method through simulation studies and compared our method with existing bi-normal approaches. We have shown that the partial area has an asymptotic normal distribution using trimmed U-statistics theory. On the basis of this asymptotic normality, we have given formulas for the confidence interval and the test statistic and we reported on their application to a genomic study of sample size approximately 10,000. Copyright 2008 John Wiley & Sons, Ltd.

Mesh:

Year:  2008        PMID: 18646265     DOI: 10.1002/sim.3335

Source DB:  PubMed          Journal:  Stat Med        ISSN: 0277-6715            Impact factor:   2.373


  6 in total

1.  Jackknife variance of the partial area under the empirical receiver operating characteristic curve.

Authors:  Andriy I Bandos; Ben Guo; David Gur
Journal:  Stat Methods Med Res       Date:  2014-09-16       Impact factor: 3.021

2.  Power calculation for comparing diagnostic accuracies in a multi-reader, multi-test design.

Authors:  Eunhee Kim; Zheng Zhang; Youdan Wang; Donglin Zeng
Journal:  Biometrics       Date:  2014-10-29       Impact factor: 2.571

3.  A unified Bayesian framework for exact inference of area under the receiver operating characteristic curve.

Authors:  Ruitao Lin; Kc Gary Chan; Haolun Shi
Journal:  Stat Methods Med Res       Date:  2021-09-01       Impact factor: 2.494

4.  Estimation of AUC or Partial AUC under Test-Result-Dependent Sampling.

Authors:  Xiaofei Wang; Junling Ma; Stephen George; Haibo Zhou
Journal:  Stat Biopharm Res       Date:  2012-10-01       Impact factor: 1.452

5.  On use of partial area under the ROC curve for evaluation of diagnostic performance.

Authors:  Hua Ma; Andriy I Bandos; Howard E Rockette; David Gur
Journal:  Stat Med       Date:  2013-03-18       Impact factor: 2.373

6.  On the use of min-max combination of biomarkers to maximize the partial area under the ROC curve.

Authors:  Hua Ma; Susan Halabi; Aiyi Liu
Journal:  J Probab Stat       Date:  2019-02-03
  6 in total

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