CoChat
Sign Up
Cited by 1,923Statistical Methods in EpidemiologyImbalanced Data Classification TechniquesMeta-analysis and systematic reviews

Principles and practical application of the receiver-operating characteristic analysis for diagnostic tests

Greiner, M, Pfeiffer, D, Smith, R.D·2000·Preventive Veterinary Medicine
DOI 10.1016/s0167-5877(00)00115-x
Open this paper in CoChat — free account

Abstract

We review the principles and practical application of receiver-operating characteristic (ROC) analysis for diagnostic tests. ROC analysis can be used for diagnostic tests with outcomes measured on ordinal, interval or ratio scales. The dependence of the diagnostic sensitivity and specificity on the selected cut-off value must be considered for a full test evaluation and for test comparison. All possible combinations of sensitivity and specificity that can be achieved by changing the test's cut-off value can be summarised using a single parameter; the area under the ROC curve. The ROC technique can also be used to optimise cut-off values with regard to a given prevalence in the target population and cost ratio of false-positive and false-negative results. However, plots of optimisation parameters against the selected cut-off value provide a more-direct method for cut-off selection. Candidates for such optimisation parameters are linear combinations of sensitivity and specificity (with weights selected to reflect the decision-making situation), odds ratio, chance-corrected measures of association (e. g. kappa) and likelihood ratios. We discuss some recent developments in ROC analysis, including meta-analysis of diagnostic tests, correlated ROC curves (paired-sample design) and chance- and prevalence-corrected ROC curves.

Integrity signals

Registry

Crossref · 10.1016/s0167-5877(00)00115-x

Cites

55 of 55 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • A cut-off score of 8 out of 30 was chosen for the SLPPDS based on the fitted ROC curve and recommendations.

    “a cut-off score of 8 out of 30 was chosen. This decision was based on the fitted ROC curve and recommendations (Greiner et al., 2000).”

    From Developing and validating the Sierra Leone perinatal psychological distress scale through an emic-etic approach (2025) · cites this paper as Greiner et al., 2000

  • ROC curves and AUC are appropriate measures to evaluate prediction capability of logistic regression models.

    “The receiver operating characteristic (ROC) curves and the areas under ROC curves (AUC) are appropriate measures to evaluate prediction capability of logistic regression models (Greiner et al., 2000; Hanley and McNeil, 1982; Meng, 2014; Zweig and Campbell, 1993).”

    From Key Enhancements to the WFRC/MAG Four-Step Travel Demand Model (2019) · cites this paper as Greiner et al., 2000

Cites

Flagged first, then by citations

Cited by

Sorted by citations

Related

Authors
Greiner, M., Pfeiffer, D., & Smith, R. D.
Affiliations
  • Freie Universität Berlin (DE)
  • Royal Veterinary College (GB)
  • University of Illinois Urbana-Champaign (US)
Corresponding author
Greiner, M
ORCID
Venue
Preventive Veterinary Medicine
Publisher
Elsevier BV
Published
2000-05
Volume
45
Issue
1-2
Pages
23-41
ISSN
0167-5877 (ISSN-L)
DOI
10.1016/s0167-5877(00)00115-x
OpenAlex
W2121943809
Cited by
1,923
References
55
Citations by year
2026: 48 · 2025: 63 · 2024: 93 · 2023: 90 · 2022: 96 · 2021: 109 · 2020: 122 · 2019: 112 · 2018: 119 · 2017: 131 · 2016: 136 · 2015: 98 · 2014: 104 · 2013: 89 · 2012: 95
FWCI
19.46
Type
journal article
Language
en
Open access
closed
Licence
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Locations
  • Preventive Veterinary Medicine (publishedVersion)
  • PubMed (publishedVersion)
Topics
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
  • Physical Sciences › Computer Science › Artificial Intelligence › Imbalanced Data Classification Techniques
  • Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
Keywords
receiver operating characteristic, diagnostic test evaluation, sensitivity and specificity, area under the ROC curve, likelihood ratios, kappa statistic, odds ratio
MeSH terms
Animals, Animal Diseases, Diagnostic Tests, Routine, Enzyme-Linked Immunosorbent Assay, Probability, ROC Curve, Sensitivity and Specificity, Software, Prevalence

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review