Evidence map›Paper›PMID 24910172›Full record

ReviewStatistics in medicine2014

Estimation of diagnostic test accuracy without full verification: a review of latent class methods.

John Collins, Minh Huynh

Abstract readReview
In one paragraph

Review in Statistics in medicine, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 57 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
57citing papers in PubMed, 3 pooled it
–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

57 citing papers in PubMed, 3 syntheses or guidelines pooled it.

  1. Pooled it
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  12. RT-qPCR Testing and Performance Metrics in the COVID-19 Era.International journal of molecular sciences · 2024
    Review
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  20. Clinically Adjudicated Reference Standards for Evaluation of Infectious Diseases Diagnostics.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2023
    Article
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

2 authors.

John CollinsRehabilitation Medicine Department, National Institutes of Health, Bethesda MD 20892, U.S.A.
Minh Huynh

Funding

Intramural NIH HHS Z99 CL999999
6 · The paper itself

Abstract

The performance of a diagnostic test is best evaluated against a reference test that is without error. For many diseases, this is not possible, and an imperfect reference test must be used. However, diagnostic accuracy estimates may be biased if inaccurately verified status is used as the truth. Statistical models have been developed to handle this situation by treating disease as a latent variable. In this paper, we conduct a systematized review of statistical methods using latent class models for estimating test accuracy and disease prevalence in the absence of complete verification.

Indexed as

BiasModels, StatisticalAnimalsDiagnostic Tests, RoutineHumansReference StandardsReproducibility of ResultsSensitivity and Specificitydiagnostic testinglatent class modelno gold standardreviewsensitivityspecificity

Identifiers

PMID24910172
PMCPMC4199084

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.