Evidence map›Paper›PMID 39485244›Full record

ArticleThe international journal of biostatistics2024

Hypothesis testing for detecting outlier evaluators.

Li Xu, David M Zucker, Molin Wang

Abstract read
In one paragraph

Article in The international journal of biostatistics, 2024. 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
–field-weighted citation impact
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.

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

3 authors.

Li XuDepartment of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.ORCID 0000-0003-2196-3722
David M ZuckerDepartment of Statistics and Data Science, Hebrew University of Jerusalem, Mt. Scopus, Jerusalem, Israel.
Molin WangDepartment of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.

Funding

What Causes Hearing Loss: Advancing the MethodsR01DC017717 · NIDCD · HARVARD UNIVERSITY D/B/A HARVARD SCHOOL OF PUBLIC HEALTH · PI Molin Wang · 2019 to 2026
$2.6M
NIDCD NIH HHS R01 DC017717
6 · The paper itself

Abstract

In epidemiological studies, the measurements of disease outcomes are carried out by different evaluators. In this paper, we propose a two-stage procedure for detecting outlier evaluators. In the first stage, a regression model is fitted to obtain the evaluators' effects. Outlier evaluators have different effects than normal evaluators. In the second stage, stepwise hypothesis tests are performed to detect outlier evaluators. The true positive rate and true negative rate of the proposed procedure are assessed in a simulation study. We apply the proposed method to detect potential outlier audiologists among the audiologists who measured hearing threshold levels of the participants in the Audiology Assessment Arm of the Conservation of Hearing Study, which is an epidemiological study for examining risk factors of hearing loss.

Indexed as

Hearing LossAuditory ThresholdComputer SimulationData Interpretation, StatisticalHumansModels, StatisticalRegression Analysisaudiometric dataevaluator outliersoutlier detectionquality control

Identifiers

PMID39485244
PMCPMC11661559

What OpenQuestion holds

Textmetadata
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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.