Evidence map›Paper›PMID 38389563›Full record

ArticleJournal of the Royal Statistical Society. Series C, Applied statistics2023

A novel agreement statistic using data on uncertainty in ratings.

Jarcy Zee, Laura Mariani, Laura Barisoni, Parag Mahajan, Brenda Gillespie

Open access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of the Royal Statistical Society. Series C, Applied statistics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.3field-weighted citation impact, top 36% 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

0 citing papers in PubMed, 1 citations in OpenAlex.

No citing paper in PubMed yet.

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

5 authors at 4 institutions in 1 country.

Jarcy ZeeUniversity of Pennsylvania Perelman School of Medicine, Department of Biostatistics, Epidemiology, and Informatics, and Children's Hospital of Philadelphia, Philadelphia, PA, USA.
Laura MarianiUniversity of Michigan, Department of Internal Medicine, Ann Arbor, MI, USA.
Laura BarisoniDuke University, Department of Pathology, Durham, NC, USA.
Parag MahajanUniversity of Delaware, Department of Economics, Newark, DE, USA.
Brenda GillespieUniversity of Michigan, Department of Biostatistics, Ann Arbor, MI, USA.
University of Michigan · USChildren's Hospital of Philadelphia · USDuke University · USUniversity of Delaware · US

Funding

Training Program in Clinical & Translationa Research in Human Glomerular DiseaseU54DK083912 · NIDDK · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI GIPSON, DEBBIE S · 2009 to 2023
$20.3M
NIDDK NIH HHS U54 DK083912
6 · The paper itself

Abstract

Many existing methods for estimating agreement correct for chance agreement by adjusting the observed proportion agreement by the probability of chance agreement based on different assumptions. These assumptions may not always be appropriate, as demonstrated by pathologists' ratings of kidney biopsy descriptors. We propose a novel agreement statistic that accounts for the empirical probability of chance agreement, estimated by collecting additional data on rater uncertainty for each rating. A standard error estimator for the proposed statistic is derived. Simulation studies show that in most cases, our proposed statistic is unbiased in estimating the probability of agreement after removing chance agreement.

Indexed as

AgreementChance AgreementRater AssessmentReproducibilityUncertainty

Identifiers

PMID38389563
PMCPMC10881211
OpenAlexW4384406484

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

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.