Evidence map›Paper›PMID 33977829›Full record

ArticleOphthalmic epidemiology2022

Tutorial on Biostatistics: Receiver-Operating Characteristic (ROC) Analysis for Correlated Eye Data.

Gui-Shuang Ying, Maureen G Maguire, Robert J Glynn, Bernard Rosner

Open access · greenAbstract read
In one paragraph

Article in Ophthalmic epidemiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed
2.1field-weighted citation impact, top 14% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

17 citing papers in PubMed, 19 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors at 2 institutions in 1 country.

Gui-Shuang YingCenter for Preventive Ophthalmology and Biostatistics, Department of Ophthalmology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Maureen G MaguireCenter for Preventive Ophthalmology and Biostatistics, Department of Ophthalmology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Robert J GlynnDivision of Preventive Medicine and the Channing Lab, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA.
Bernard RosnerDivision of Preventive Medicine and the Channing Lab, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA.
Brigham and Women's Hospital · USUniversity of Pennsylvania · US

Funding

Scientific TransparencyP30EY001583 · NEI · UNIVERSITY OF PENNSYLVANIA · PI Qi N Cui · 1985 to 2026
$19.5M
Use of Correlated Data Methods in OphthalmologyR01EY022445 · NEI · BRIGHAM AND WOMEN'S HOSPITAL · PI ROSNER, BERNARD A · 2013 to 2025
$4.6M
NEI NIH HHS P30 EY001583NEI NIH HHS R01 EY022445
6 · The paper itself

Abstract

purposeTo demonstrate methods for receiver-operating characteristic (ROC) analysis of correlated eye data.

methodsWe applied the Obuchowski's nonparametric approach and cluster bootstrap for estimating and comparing the area under ROC curve (AUC) between different sets of predictors to three datasets with varying inter-eye correlation.

resultsIn an optic neuritis (ON) study of 152 eyes (80 patients), the AUC of optical coherence tomography retinal nerve fiber layer thickness for diagnosing ON (inter-eye kappa = 0.13) was 0.71 [95% confidence interval (95% CI): 0.622, 0.792] from the naïve approach without accounting for inter-eye correlation was narrower than from nonparametric (95% CI: 0.613, 0.801) or cluster bootstrap (95% CI: 0.614, 0.797) approaches. In an analysis of 198 eyes (135 patients), the baseline Age-related Eye disease Study scale predicted 5-year incidence of advanced age-related macular degeneration (inter-eye kappa = 0.23) with AUC of 0.72. The 95% CI from the naïve approach was slightly narrower (0.645, 0.794) than from the nonparametric (0.641, 0.797) or cluster bootstrap (0.641, 0.793) approaches. In an analysis of 1542 eyes (771 infants), birthweight and gestational age predicted treatment-requiring retinopathy of prematurity (inter-eye kappa = 0.98) with AUC of 0.80. Furthermore, the 95% CI from the naïve approach was narrower (0.769, 0.835) than from the nonparametric (0.755, 0.848) or cluster bootstrap (0.755, 0.845) approaches. 95% CIs for AUC differences between different models were narrower in the naïve approach than the nonparametric or cluster bootstrap approaches.

conclusionIn ROC analysis of correlated eye data, ignoring inter-eye correlation leads to narrower 95% CI with underestimation dependent on magnitude of inter-eye correlation. Nonparametric and cluster bootstrap approaches properly account for inter-eye correlation.

Indexed as

Optic NeuritisTomography, Optical CoherenceArea Under CurveHumansInfantInfant, NewbornRetinaROC Curvearea under ROC curvecluster bootstrapcorrelated eye dataOcular testROC analysis

Identifiers

PMID33977829
PMCPMC8586066
OpenAlexW3161625148

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.