Literature DB >> 16345033

ROC analysis for markers with mass at zero.

Enrique F Schisterman1, Benjamin Reiser, David Faraggi.   

Abstract

The receiver operating characteristic (ROC) curve and in particular the area under the curve (AUC) is commonly used to examine the discriminatory ability of diagnostic markers. Certain markers while basically continuous and non-negative have a positive probability mass (spike) at the value zero. We discuss a flexible modelling approach to such data and contrast it with the standard non-parametric approach. We show how the modelling approach can be extended to take account of the effect of explanatory variables. We motivate this problem and illustrate the modelling approach using data on the coronary calcium score, measured by electron beam tomography, which is a marker for atherosclerosis. Copyright 2005 John Wiley & Sons, Ltd.

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Year:  2006        PMID: 16345033     DOI: 10.1002/sim.2301

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


  7 in total

1.  Parametric and non-parametric confidence intervals of the probability of identifying early disease stage given sensitivity to full disease and specificity with three ordinal diagnostic groups.

Authors:  Tuochuan Dong; Lili Tian; Alan Hutson; Chengjie Xiong
Journal:  Stat Med       Date:  2011-12-05       Impact factor: 2.373

2.  Youden Index and the optimal threshold for markers with mass at zero.

Authors:  Enrique F Schisterman; David Faraggi; Benjamin Reiser; Jessica Hu
Journal:  Stat Med       Date:  2008-01-30       Impact factor: 2.373

3.  Estimating the odds ratio when exposure has a limit of detection.

Authors:  Stephen R Cole; Haitao Chu; Lei Nie; Enrique F Schisterman
Journal:  Int J Epidemiol       Date:  2009-08-10       Impact factor: 7.196

4.  A random-sum Wilcoxon statistic and its application to analysis of ROC and LROC data.

Authors:  Liansheng Larry Tang; N Balakrishnan
Journal:  J Stat Plan Inference       Date:  2010-06-12       Impact factor: 1.111

5.  Confidence interval estimation of the difference between two sensitivities to the early disease stage.

Authors:  Tuochuan Dong; Le Kang; Alan Hutson; Chengjie Xiong; Lili Tian
Journal:  Biom J       Date:  2013-11-22       Impact factor: 2.207

6.  Estimation and construction of confidence intervals for biomarker cutoff-points under the shortest Euclidean distance from the ROC surface to the perfection corner.

Authors:  Brian R Mosier; Leonidas E Bantis
Journal:  Stat Med       Date:  2021-06-03       Impact factor: 2.497

7.  Effect size measures and their benchmark values for quantifying benefit or risk of medicinal products.

Authors:  Volker Rahlfs; Helmuth Zimmermann
Journal:  Biom J       Date:  2019-02-28       Impact factor: 2.207

  7 in total

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