Evidence map›Paper›PMID 33990992›Full record

ArticleStatistics in medicine2021

On the robustness of latent class models for diagnostic testing with no gold standard.

Matthew R Schofield, Michael J Maze, John A Crump, Matthew P Rubach, Renee Galloway, Katrina J Sharples

Abstract read
In one paragraph

Article in Statistics in medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed, 1 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

10 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors.

Matthew R SchofieldDepartment of Mathematics and Statistics, University of Otago, Dunedin, New Zealand.ORCID 0000-0003-1481-2766
Michael J MazeCentre for International Health, University of Otago, Dunedin, New Zealand.
John A CrumpDepartment of Preventive and Social Medicine, University of Otago, Dunedin, New Zealand.ORCID 0000-0002-4529-102X
Matthew P RubachDivision of Infectious Diseases, Duke University, Durham, North Carolina, USA.
Renee GallowayBacterial Special Pathogens Branch, US Center for Disease Control and Prevention, Atlanta, Georgia, USA.
Katrina J SharplesDepartment of Mathematics and Statistics, University of Otago, Dunedin, New Zealand.

Funding

University of California Global Health Institute Program for Fellows and ScholarsR25TW009343 · FIC · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI COHEN, CRAIG R, STRATHDEE, STEFFANIE A. · 2012 to 2017
$6.4M
Vanderbilt-Emory-Cornell-Duke Consortium for Global Health Fellows (VECDor)R25TW009337 · FIC · VANDERBILT UNIVERSITY MEDICAL CENTER · PI HEIMBURGER, DOUGLAS CORBETT, VERMUND, STEN H. · 2012 to 2016
$6.3M
Investigating Febrile Deaths in Tanzania (INDITe)R01AI121378 · NIAID · DUKE UNIVERSITY · PI RUBACH, MATTHEW P · 2016 to 2020
$2.7M
The Impact and Society Ecology of Bacterial Zoonoses in Northern Tanzania: UnderR01TW009237 · FIC · DUKE UNIVERSITY · PI CRUMP, JOHN ANDREW · 2011 to 2014
$1.7M
Viral Zoonoses and Severe Febrile Illness in Northern TanzaniaK23AI116869 · NIAID · DUKE UNIVERSITY · PI RUBACH, MATTHEW P · 2016 to 2020
$896k
Biotechnology and Biological Sciences Research Council BB/J010367/1Biotechnology and Biological Sciences Research Council BB/L017679Biotechnology and Biological Sciences Research Council BB/L018845Biotechnology and Biological Sciences Research Council BB/L018926Biotechnology and Biological Sciences Research Council BB/L018926/1FIC NIH HHS R01 TW009237FIC NIH HHS R25 TW009337FIC NIH HHS R25 TW009343NIAID NIH HHS K23 AI116869NIAID NIH HHS R01 AI121378
6 · The paper itself

Abstract

It is difficult to estimate sensitivity and specificity of diagnostic tests when there is no gold standard. Latent class models have been proposed as a potential solution as they provide estimates without the need for a gold standard. Using a motivating example of the evaluation of point of care tests for leptospirosis in Tanzania, we show how a realistic violation of assumptions underpinning the latent class model can lead directly to substantial bias in the estimates of the parameters of interest. In particular, we consider the robustness of estimates of sensitivity, specificity, and prevalence, to the presence of additional latent states when fitting a two-state latent class model. The violation is minor in the sense that it cannot be routinely detected with goodness-of-fit procedures, but is major with regard to the resulting bias.

Indexed as

Diagnostic Tests, RoutineModels, StatisticalBayes TheoremBiasHumansLatent Class AnalysisPrevalenceSensitivity and SpecificityBayesleptospirosismodel sensitivitysensitivityspecificity

Identifiers

PMID33990992
PMCPMC8440412

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Registered trials

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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.