Literature DB >> 16135934

Standardizing diagnostic markers to evaluate and compare their performance.

Margaret Sullivan Pepe1, Gary Longton.   

Abstract

BACKGROUND: Markers that purport to distinguish subjects with a condition from those without a condition must be evaluated rigorously for their classification accuracy. A single approach for statistical evaluation and comparison of markers is not yet established.
METHODS: We suggest a standardization that uses the marker distribution in unaffected subjects as a reference. For an affected subject with marker value Y, the standardized placement value is the proportion of unaffected subjects with marker values that exceed Y.
RESULTS: We applied the standardization to 2 illustrative datasets. As a marker for pancreatic cancer, the CA-19-9 marker had smaller placement values than the CA-125 marker, indicating that CA-19-9 was the better marker. For detecting hearing impairment, the placement values for the test output (the marker) were smaller when the input sound stimulus was of lower intensity, which indicates that the test better distinguishes hearing-impaired from unimpaired ears when a lower intensity sound stimulus is used. Explicit connections are drawn between the distribution of standardized marker values and the receiver operating characteristic curve, one established statistical technique for evaluating classifiers.
CONCLUSION: The standardization is an intuitive procedure for evaluating markers. It facilitates direct and meaningful comparisons between markers. It also provides a new view of receiver operating characteristic analysis that may render it more accessible to those as yet unfamiliar with it. The general approach provides a statistical tool to address important questions that are typically not addressed in current marker research, such as quantifying and controlling for covariate effects.

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Year:  2005        PMID: 16135934     DOI: 10.1097/01.ede.0000173041.03470.8b

Source DB:  PubMed          Journal:  Epidemiology        ISSN: 1044-3983            Impact factor:   4.822


  33 in total

1.  Estimating the diagnostic likelihood ratio of a continuous marker.

Authors:  Wen Gu; Margaret Sullivan Pepe
Journal:  Biostatistics       Date:  2010-07-16       Impact factor: 5.899

2.  Multiplex targeted proteomic assay for biomarker detection in plasma: a pancreatic cancer biomarker case study.

Authors:  Sheng Pan; Ru Chen; Randall E Brand; Sarah Hawley; Yasuko Tamura; Philip R Gafken; Brian P Milless; David R Goodlett; John Rush; Teresa A Brentnall
Journal:  J Proteome Res       Date:  2012-02-08       Impact factor: 4.466

3.  Biomarker evaluation and comparison using the controls as a reference population.

Authors:  Ying Huang; Margaret Sullivan Pepe
Journal:  Biostatistics       Date:  2008-08-28       Impact factor: 5.899

Review 4.  Discovery and validation of graft-versus-host disease biomarkers.

Authors:  Sophie Paczesny
Journal:  Blood       Date:  2012-11-19       Impact factor: 22.113

Review 5.  Proteomics analysis of bodily fluids in pancreatic cancer.

Authors:  Sheng Pan; Teresa A Brentnall; Ru Chen
Journal:  Proteomics       Date:  2015-04-27       Impact factor: 3.984

Review 6.  Graft-versus-host disease biomarkers: omics and personalized medicine.

Authors:  Sophie Paczesny; Nisha Raiker; Sam Brooks; Christy Mumaw
Journal:  Int J Hematol       Date:  2013-09       Impact factor: 2.490

7.  Ovarian cancer biomarker performance in prostate, lung, colorectal, and ovarian cancer screening trial specimens.

Authors:  Daniel W Cramer; Robert C Bast; Christine D Berg; Eleftherios P Diamandis; Andrew K Godwin; Patricia Hartge; Anna E Lokshin; Karen H Lu; Martin W McIntosh; Gil Mor; Christos Patriotis; Paul F Pinsky; Mark D Thornquist; Nathalie Scholler; Steven J Skates; Patrick M Sluss; Sudhir Srivastava; David C Ward; Zhen Zhang; Claire S Zhu; Nicole Urban
Journal:  Cancer Prev Res (Phila)       Date:  2011-03

8.  Comparison of the diagnostic accuracies of the Spectralis, Cirrus, and RTVue optical coherence tomography devices in glaucoma.

Authors:  Mauro T Leite; Harsha L Rao; Linda M Zangwill; Robert N Weinreb; Felipe A Medeiros
Journal:  Ophthalmology       Date:  2011-03-05       Impact factor: 12.079

9.  Effects of personal characteristics on serum CA125, mesothelin, and HE4 levels in healthy postmenopausal women at high-risk for ovarian cancer.

Authors:  Kimberly A Lowe; Chirag Shah; Erin Wallace; Garnet Anderson; Pamela Paley; Martin McIntosh; M Robyn Andersen; Nathalie Scholler; Lindsay Bergan; Jason Thorpe; Nicole Urban; Charles W Drescher
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2008-09       Impact factor: 4.254

10.  Influence of ovarian cancer risk status on the diagnostic performance of the serum biomarkers mesothelin, HE4, and CA125.

Authors:  Chirag A Shah; Kimberly A Lowe; Pamela Paley; Erin Wallace; Garnet L Anderson; Martin W McIntosh; M Robyn Andersen; Nathalie Scholler; Lindsay A Bergan; Jason D Thorpe; Nicole Urban; Charles W Drescher
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2009-05       Impact factor: 4.254

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