Literature DB >> 11447052

Statistical inference for the area under the receiver operating characteristic curve in the presence of random measurement error.

E F Schisterman1, D Faraggi, B Reiser, M Trevisan.   

Abstract

The area under the receiver operating characteristic curve is the most commonly used measure of the ability of a biomarker to distinguish between two populations. Some markers are subject to substantial measurement error. Under normality assumptions, the authors develop a confidence interval procedure for the area under the receiver operating characteristic curve that adjusts for measurement error. This procedure assumes the availability of data from a reliability study of the biomarker. A simulation study was used to check the validity of the proposed confidence interval. Furthermore, it was shown that not adjusting for measurement error could result in a serious understatement of the effectiveness of the biomarker.

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Year:  2001        PMID: 11447052     DOI: 10.1093/aje/154.2.174

Source DB:  PubMed          Journal:  Am J Epidemiol        ISSN: 0002-9262            Impact factor:   4.897


  18 in total

1.  Memorial Sloan-Kettering Cancer Center Nomogram to predict the risk of non-sentinel lymph node metastasis in Japanese breast cancer patients.

Authors:  Tatsunari Sasada; Shigeru Murakami; Tsuyoshi Kataoka; Masahiro Ohara; Shinji Ozaki; Morihito Okada; Hideki Ohdan
Journal:  Surg Today       Date:  2011-12-14       Impact factor: 2.549

2.  Estimation of ROC curves based on stably distributed biomarkers subject to measurement error and pooling mixtures.

Authors:  Albert Vexler; Enrique F Schisterman; Aiyi Liu
Journal:  Stat Med       Date:  2008-01-30       Impact factor: 2.373

3.  Criminogenic factors, psychotic symptoms, and incident arrests among people with serious mental illnesses under intensive outpatient treatment.

Authors:  Seth J Prins; Jennifer L Skeem; Christine Mauro; Bruce G Link
Journal:  Law Hum Behav       Date:  2014-08-18

4.  A general framework for the regression analysis of pooled biomarker assessments.

Authors:  Yan Liu; Christopher McMahan; Colin Gallagher
Journal:  Stat Med       Date:  2017-03-28       Impact factor: 2.373

5.  Evaluation of Cerebrospinal Fluid Assay Variability in Alzheimer's Disease.

Authors:  Matthew T White; Leslie M Shaw; Sharon X Xie
Journal:  J Alzheimers Dis       Date:  2016       Impact factor: 4.472

6.  To pool or not to pool, from whether to when: applications of pooling to biospecimens subject to a limit of detection.

Authors:  Enrique F Schisterman; Albert Vexler
Journal:  Paediatr Perinat Epidemiol       Date:  2008-09       Impact factor: 3.980

7.  Generalized ROC curve inference for a biomarker subject to a limit of detection and measurement error.

Authors:  Neil J Perkins; Enrique F Schisterman; Albert Vexler
Journal:  Stat Med       Date:  2009-06-15       Impact factor: 2.373

8.  Adjustment for measurement error in evaluating diagnostic biomarkers by using an internal reliability sample.

Authors:  Matthew T White; Sharon X Xie
Journal:  Stat Med       Date:  2013-06-14       Impact factor: 2.373

9.  Hybrid pooled-unpooled design for cost-efficient measurement of biomarkers.

Authors:  Enrique F Schisterman; Albert Vexler; Sunni L Mumford; Neil J Perkins
Journal:  Stat Med       Date:  2010-02-28       Impact factor: 2.373

10.  Association of neck circumference and obesity status with elevated blood pressure in children.

Authors:  O O Nafiu; A Zepeda; C Curcio; Y Prasad
Journal:  J Hum Hypertens       Date:  2013-10-03       Impact factor: 3.012

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