Continued controversy in using latent class models for estimating diagnostic accuracy without a gold standard

2021 ◽  
Vol 40 (22) ◽  
pp. 4764-4765
Author(s):  
Paul S. Albert
Keyword(s):  
Diagnostic Accuracy ◽  
Gold Standard ◽  
Latent Class ◽  
Latent Class Models ◽  
Class Models ◽  
Without A Gold Standard

scholarly journals Problems in detecting misfit of latent class models in diagnostic research without a gold standard were shown

2016 ◽  
Vol 74 ◽  
pp. 158-166 ◽  
Author(s):  
Maarten van Smeden ◽  
Daniel L. Oberski ◽  
Johannes B. Reitsma ◽  
Jeroen K. Vermunt ◽  
Karel G.M. Moons ◽  
...  
Keyword(s):  
Gold Standard ◽  
Latent Class ◽  
Latent Class Models ◽  
Diagnostic Research ◽  
Class Models ◽  
Without A Gold Standard

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

2021 ◽  
Author(s):  
Matthew R. Schofield ◽  
Michael J. Maze ◽  
John A. Crump ◽  
Matthew P. Rubach ◽  
Renee Galloway ◽  
...  
Keyword(s):  
Gold Standard ◽  
Latent Class ◽  
Diagnostic Testing ◽  
Latent Class Models ◽  
No Gold Standard ◽  
Class Models

STARD-BLCM: Standards for the Reporting of Diagnostic accuracy studies that use Bayesian Latent Class Models

2017 ◽  
Vol 138 ◽  
pp. 37-47 ◽  
Author(s):  
Polychronis Kostoulas ◽  
Søren S. Nielsen ◽  
Adam J. Branscum ◽  
Wesley O. Johnson ◽  
Nandini Dendukuri ◽  
...  
Keyword(s):  
Diagnostic Accuracy ◽  
Latent Class ◽  
Latent Class Models ◽  
Class Models

Rejoinder to “On the robustness of latent class models for diagnostic testing with no gold standard”

2021 ◽  
Vol 40 (22) ◽  
pp. 4770-4771
Author(s):  
Matthew R. Schofield ◽  
Michael J. Maze ◽  
John A. Crump ◽  
Matthew P. Rubach ◽  
Renee L. Galloway ◽  
...  
Keyword(s):  
Gold Standard ◽  
Latent Class ◽  
Diagnostic Testing ◽  
Latent Class Models ◽  
No Gold Standard ◽  
Class Models

scholarly journals Different latent class models were used and evaluated for assessing the accuracy of campylobacter diagnostic tests: overcoming imperfect reference standards?

2018 ◽  
Vol 146 (12) ◽  
pp. 1556-1564 ◽  
Author(s):  
J. Asselineau ◽  
A. Paye ◽  
E. Bessède ◽  
P. Perez ◽  
C. Proust-Lima
Keyword(s):  
Diagnostic Accuracy ◽  
Diagnostic Tests ◽  
Goodness Of Fit ◽  
Latent Class ◽  
Latent Class Models ◽  
Reference Standard ◽  
Promising Alternative ◽  
Data Sparseness ◽  
Campylobacter Infection ◽  
Class Models

AbstractIn the absence of perfect reference standard, classical techniques result in biased diagnostic accuracy and prevalence estimates. By statistically defining the true disease status, latent class models (LCM) constitute a promising alternative. However, LCM is a complex method which relies on parametric assumptions, including usually a conditional independence between tests and might suffer from data sparseness. We carefully applied LCMs to assess new campylobacter infection detection tests for which bacteriological culture is an imperfect reference standard. Five diagnostic tests (culture, polymerase chain reaction and three immunoenzymatic tests) of campylobacter infection were collected in 623 patients from Bordeaux and Lyon Hospitals, France. Their diagnostic accuracy were estimated with standard and extended LCMs with a thorough examination of models goodness-of-fit. The model including a residual dependence specific to the immunoenzymatic tests best complied with LCM assumptions. Asymptotic results of goodness-of-fit statistics were substantially impaired by data sparseness and empirical distributions were preferred. Results confirmed moderate sensitivity of the culture and high performances of immunoenzymatic tests. LCMs can be used to estimate diagnostic tests accuracy in the absence of perfect reference standard. However, their implementation and assessment require specific attention due to data sparseness and limitations of existing software.

scholarly journals Diagnostic accuracy estimates for COVID-19 RT-PCR and Lateral flow immunoassay tests with Bayesian latent class models

2020 ◽  
Author(s):  
Polychronis Kostoulas ◽  
Paolo Eusebi ◽  
Sonja Hartnack
Keyword(s):  
Diagnostic Accuracy ◽  
Symptom Onset ◽  
Latent Class ◽  
Reference Method ◽  
Latent Class Models ◽  
Lateral Flow ◽  
Lateral Flow Immunoassay ◽  
Rt Pcr ◽  
The Cross ◽  
Class Models

Abstract The objective of this work was to estimate the diagnostic accuracy of RT-PCR and Lateral flow immunoassay tests (LFIA) for COVID-19, depending on the time post symptom onset. Based on the cross-classified results of RT-PCR and LFIA, we used Bayesian latent class models (BLCMs), which do not require a gold standard for the evaluation of diagnostics. Data were extracted from studies that evaluated LFIA (IgG and/or IgM) assays using RT-PCR as the reference method. The cross-classified results of LFIA and RT-PCR were analysed separately for the first, second and third week post symptom onset. The SeRT-PCR was 0.695 (95% probability intervals: 0.563; 0.837) for the first week and remained similar for the second and the third week. The SeIgG/M was 0.318 (0.229; 0.416) for the first week and increased steadily. It was 0.755 (0.673; 0.829) and 0.927 (0.881; 0.965) for the second and third week, respectively. Both tests had a high to absolute Sp, with point median estimates for SpRT-PCR being consistently higher. SpRT-PCR was 0.990 (0.980; 0.998) for the first week. The corresponding value for SpIgG/M was 0.962 (0.905; 0.998). Further, Sp estimates for each test did not differ between weeks. BLCMs provide a valid and efficient alternative for evaluating the rapidly evolving diagnostics for COVID-19, under various clinical settings and for different risk profiles.

scholarly journals Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) seroprevalence: Navigating the absence of a gold standard

2021 ◽  
Author(s):  
Sahar Saeed ◽  
Sheila F O'Brein ◽  
Kento Abe ◽  
QiLong Yi ◽  
Bhavisha Rathod ◽  
...  
Keyword(s):  
Gold Standard ◽  
Latent Class ◽  
Credible Interval ◽  
Cross Sectional Study ◽  
Latent Class Models ◽  
Qualitative Assessment ◽  
Cross Sectional ◽  
June July ◽  
No Gold Standard ◽  
Class Models

Background: Multiple anti-SARS-CoV-2 immunoassays are available, but no gold standard exists. We assessed four assays using various methodological approaches to estimate SARS-COV-2 seroprevalence during the first COVID-19 wave in Canada. Methods: This serial cross-sectional study was conducted using plasma samples from healthy blood donors between April-September 2020. Qualitative assessment of SARS-CoV-2 IgG antibodies was based on four assays: Abbott Architect SARS-Cov-2 IgG assay (target nucleocapsid) (Abbott-NP) and three in-house IgG ELISA assays (target spike glycoprotein (Spike), spike receptor binding domain (RBD), and nucleocapsid (NP)). Seroprevalence was estimated using multiple composite reference standards (CRS) and by a series of Bayesian Latent Class Models (BLCM) (using uninformative, weakly, and informative priors). Results: 8999 blood samples were tested. The Abbott-NP assay consistently estimated seroprevalence to be lower than the ELISA-based assays. Discordance between assays was common, 13 unique diagnostic phenotypes were observed. Only 32 samples (0.4%) were positive by all four assays. BLCM using uninformative priors predicted seroprevalence increased from 0.7% (95% credible interval (CrI); 0.4, 1.0%) in April/May to 0.8% (95% CrI 0.5, 1.2%) in June/July to 1.1% (95% CrI 0.7, 1.6) in August/September. Results from CRS were very similar to the BLCM. Assay characteristics varied considerably over time. Overall spike had the highest sensitivity (89.1% (95% CrI 79.2, 96.9%), while the sensitivity of the Abbott-NP assay waned from 65.3% (95% CrI 43.6, 85.0%) in April/May to 45.9% (95% CrI 27.8, 65.6) by August/September. Discussion: We found low SARS-CoV-2 seroprevalence rates at the end of the first wave and estimates derived from single assays may be biased.

Sign in / Sign up

Export Citation Format

Share Document