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

PLoS ONE ◽  
2021 ◽  
Vol 16 (9) ◽  
pp. e0257743
Author(s):  
Sahar Saeed ◽  
Sheila F. O’Brien ◽  
Kento Abe ◽  
Qi-Long Yi ◽  
Bhavisha Rathod ◽  
...  
Keyword(s):  
Severe Acute Respiratory Syndrome ◽  
Gold Standard ◽  
Latent Class ◽  
Latent Class Model ◽  
Credible Interval ◽  
Latent Class Models ◽  
Igg Antibody ◽  
Cross Sectional ◽  
Class Models ◽  
Over Time

Background Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) seroprevalence studies bridge the gap left from case detection, to estimate the true burden of the COVID-19 pandemic. While multiple anti-SARS-CoV-2 immunoassays are available, no gold standard exists. Methods This serial cross-sectional study was conducted using plasma samples from 8999 healthy blood donors between April-September 2020. Each sample was tested by four assays: Abbott SARS-Cov-2 IgG assay, targeting nucleocapsid (Abbott-NP) and three in-house IgG ELISA assays (targeting spike glycoprotein, receptor binding domain, and nucleocapsid). Seroprevalence rates were compared using multiple composite reference standards and by a series of Bayesian Latent Class Models. Result We found 13 unique diagnostic phenotypes; only 32 samples (0.4%) were positive by all assays. None of the individual assays resulted in seroprevalence increasing monotonically over time. In contrast, by using the results from all assays, the Bayesian Latent Class Model with informative priors predicted seroprevalence increased from 0.7% (95% credible interval (95% CrI); 0.4, 1.0%) in April/May to 0.7% (95% CrI 0.5, 1.1%) in June/July to 0.9% (95% CrI 0.5, 1.3) in August/September. Assay characteristics varied over time. Overall Spike had the highest sensitivity (93.5% (95% CrI 88.7, 97.3%), while the sensitivity of the Abbott-NP assay waned from 77.3% (95% CrI 58.7, 92.5%) in April/May to 64.4% (95% CrI 45.6, 83.0) by August/September. Discussion Our results confirmed very low seroprevalence after the first wave in Canada. Given the dynamic nature of this pandemic, Bayesian Latent Class Models can be used to correct for imperfect test characteristics and waning IgG antibody signals.

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.

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

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

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

Bayesian nonparametric latent class model for longitudinal data

2020 ◽  
Vol 29 (11) ◽  
pp. 3381-3395
Author(s):  
Wonmo Koo ◽  
Heeyoung Kim
Keyword(s):  
Longitudinal Data ◽  
Latent Class ◽  
Latent Class Model ◽  
Latent Class Models ◽  
Model Parameters ◽  
Latent Classes ◽  
Bayesian Nonparametric ◽  
Class Model ◽  
Proposed Model ◽  
Class Models

Latent class models have been widely used in longitudinal studies to uncover unobserved heterogeneity in a population and find the characteristics of the latent classes simultaneously using the class allocation probabilities dependent on predictors. However, previous latent class models for longitudinal data suffer from uncertainty in the choice of the number of latent classes. In this study, we propose a Bayesian nonparametric latent class model for longitudinal data, which allows the number of latent classes to be inferred from the data. The proposed model is an infinite mixture model with predictor-dependent class allocation probabilities; an individual longitudinal trajectory is described by the class-specific linear mixed effects model. The model parameters are estimated using Markov chain Monte Carlo methods. The proposed model is validated using a simulated example and a real-data example for characterizing latent classes of estradiol trajectories over the menopausal transition using data from the Study of Women’s Health Across the Nation.

Latent Class Models for the Analysis of Behavioral Hierarchies

1987 ◽  
Vol 24 (2) ◽  
pp. 174-186 ◽  
Author(s):  
Lawrence F. Feick
Keyword(s):  
Item Response ◽  
Latent Class ◽  
Latent Class Models ◽  
Scale Model ◽  
Guttman Scale ◽  
Cross Sectional Survey ◽  
Cross Sectional ◽  
Consumer Complaint ◽  
Item Responses ◽  
Class Models

The author develops and describes latent class models useful for the analysis of behavioral hierarchies. The latent class models investigated are generalizations of the Guttman scale model and consider probabilistic relationships of item response to scale type and multiple hierarchical orderings of item responses. In addition, the author develops models for hierarchies that are present at the level of sets of items rather than at the level of individual items. He calls them “characteristics models” and examines their relationship to models for hierarchies of items. The models are illustrated on consumer complaint data gathered from a cross-sectional survey.

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