Estimates of sensitivity and specificity of serological tests for SARS-CoV-2 specific antibodies using a Bayesian latent class model approach

Detalhes bibliográficos
Autor(a) principal: Costa, JP
Data de Publicação: 2024
Outros Autores: Meireles, P, Meletis, E, Kostoulas, P, Severo, M
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: https://hdl.handle.net/10216/157573
Resumo: "Objectives: Assessing the accuracy of serological tests for SARS-CoV-2 was challenging due to the lack of a gold standard. This study aimed to estimate the accuracy of SARS-CoV-2-specific serological tests using Bayesian latent class models (BLCM) and compare methods with and without a gold standard. Study design and setting: In this study, we analyzed 356 samples-254 positives, ie, from individuals with a previous SARS-CoV-2 infection diagnosis, and 102 negatives, ie, prepandemic samples-using six different rapid serological tests and one laboratory assay. A BLCM was employed to concurrently estimate the sensitivity and specificity of all serological tests for the immunoglobulin (Ig) M and IgG antibodies specific for SARS-CoV-2. Noninformative priors were used. A sensitivity analysis was conducted considering three methods: 1) reverse transcription-polymerase chain reaction test (RT-PCR) as the gold standard, 2) BLCM with RT-PCR as an imperfect gold standard, and 3) frequentist latent class model (LCM). All analyses used software R version 4.3.0, and BLCM were fitted using package runjags using the software JAGS (Just Another Gibbs Sampler). Results: The BLCM-derived sensitivity for IgM varied from 10.7% [95% credibility interval (CrI):1.9-24.6] to 96.9% (95% CrI: 91.0-100.0), with specificities ranging from 48.3% (95% CrI: 39.0-57.6) to 98.9% (95% CrI: 96.2-100.0). Sensitivity for IgG varied between 76.9% (95% CrI: 68.2-84.7) and 99.1% (95% CrI: 96.1-100.0), and specificity ranged from 49.9% (95% CrI: 19.4-95.8) to 99.3% (95% CrI: 97.2-100.0). LCM results were comparable to BLCM. Considering the RT-PCR as a gold standard underestimated the tests' sensitivity, particularly for IgM. Conclusion: BLCM-derived results deviated from those using a gold standard, which underestimated the tests' characteristics, particularly sensitivity. Although Bayesian and frequentist LCM approaches yielded comparable results, BLCM had the benefit of enabling credibility interval computation even when sample power is limited."
id RCAP_9ef7969cf1c4fa2adc2427f4a0cb8c91
oai_identifier_str oai:repositorio-aberto.up.pt:10216/157573
network_acronym_str RCAP
network_name_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository_id_str 7160
spelling Estimates of sensitivity and specificity of serological tests for SARS-CoV-2 specific antibodies using a Bayesian latent class model approach"Objectives: Assessing the accuracy of serological tests for SARS-CoV-2 was challenging due to the lack of a gold standard. This study aimed to estimate the accuracy of SARS-CoV-2-specific serological tests using Bayesian latent class models (BLCM) and compare methods with and without a gold standard. Study design and setting: In this study, we analyzed 356 samples-254 positives, ie, from individuals with a previous SARS-CoV-2 infection diagnosis, and 102 negatives, ie, prepandemic samples-using six different rapid serological tests and one laboratory assay. A BLCM was employed to concurrently estimate the sensitivity and specificity of all serological tests for the immunoglobulin (Ig) M and IgG antibodies specific for SARS-CoV-2. Noninformative priors were used. A sensitivity analysis was conducted considering three methods: 1) reverse transcription-polymerase chain reaction test (RT-PCR) as the gold standard, 2) BLCM with RT-PCR as an imperfect gold standard, and 3) frequentist latent class model (LCM). All analyses used software R version 4.3.0, and BLCM were fitted using package runjags using the software JAGS (Just Another Gibbs Sampler). Results: The BLCM-derived sensitivity for IgM varied from 10.7% [95% credibility interval (CrI):1.9-24.6] to 96.9% (95% CrI: 91.0-100.0), with specificities ranging from 48.3% (95% CrI: 39.0-57.6) to 98.9% (95% CrI: 96.2-100.0). Sensitivity for IgG varied between 76.9% (95% CrI: 68.2-84.7) and 99.1% (95% CrI: 96.1-100.0), and specificity ranged from 49.9% (95% CrI: 19.4-95.8) to 99.3% (95% CrI: 97.2-100.0). LCM results were comparable to BLCM. Considering the RT-PCR as a gold standard underestimated the tests' sensitivity, particularly for IgM. Conclusion: BLCM-derived results deviated from those using a gold standard, which underestimated the tests' characteristics, particularly sensitivity. Although Bayesian and frequentist LCM approaches yielded comparable results, BLCM had the benefit of enabling credibility interval computation even when sample power is limited."Elsevier20242024-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/10216/157573eng0895-435610.1016/j.jclinepi.2024.111267Costa, JPMeireles, PMeletis, EKostoulas, PSevero, Minfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-08T01:19:34Zoai:repositorio-aberto.up.pt:10216/157573Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:13:50.801199Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Estimates of sensitivity and specificity of serological tests for SARS-CoV-2 specific antibodies using a Bayesian latent class model approach
title Estimates of sensitivity and specificity of serological tests for SARS-CoV-2 specific antibodies using a Bayesian latent class model approach
spellingShingle Estimates of sensitivity and specificity of serological tests for SARS-CoV-2 specific antibodies using a Bayesian latent class model approach
Costa, JP
title_short Estimates of sensitivity and specificity of serological tests for SARS-CoV-2 specific antibodies using a Bayesian latent class model approach
title_full Estimates of sensitivity and specificity of serological tests for SARS-CoV-2 specific antibodies using a Bayesian latent class model approach
title_fullStr Estimates of sensitivity and specificity of serological tests for SARS-CoV-2 specific antibodies using a Bayesian latent class model approach
title_full_unstemmed Estimates of sensitivity and specificity of serological tests for SARS-CoV-2 specific antibodies using a Bayesian latent class model approach
title_sort Estimates of sensitivity and specificity of serological tests for SARS-CoV-2 specific antibodies using a Bayesian latent class model approach
author Costa, JP
author_facet Costa, JP
Meireles, P
Meletis, E
Kostoulas, P
Severo, M
author_role author
author2 Meireles, P
Meletis, E
Kostoulas, P
Severo, M
author2_role author
author
author
author
dc.contributor.author.fl_str_mv Costa, JP
Meireles, P
Meletis, E
Kostoulas, P
Severo, M
description "Objectives: Assessing the accuracy of serological tests for SARS-CoV-2 was challenging due to the lack of a gold standard. This study aimed to estimate the accuracy of SARS-CoV-2-specific serological tests using Bayesian latent class models (BLCM) and compare methods with and without a gold standard. Study design and setting: In this study, we analyzed 356 samples-254 positives, ie, from individuals with a previous SARS-CoV-2 infection diagnosis, and 102 negatives, ie, prepandemic samples-using six different rapid serological tests and one laboratory assay. A BLCM was employed to concurrently estimate the sensitivity and specificity of all serological tests for the immunoglobulin (Ig) M and IgG antibodies specific for SARS-CoV-2. Noninformative priors were used. A sensitivity analysis was conducted considering three methods: 1) reverse transcription-polymerase chain reaction test (RT-PCR) as the gold standard, 2) BLCM with RT-PCR as an imperfect gold standard, and 3) frequentist latent class model (LCM). All analyses used software R version 4.3.0, and BLCM were fitted using package runjags using the software JAGS (Just Another Gibbs Sampler). Results: The BLCM-derived sensitivity for IgM varied from 10.7% [95% credibility interval (CrI):1.9-24.6] to 96.9% (95% CrI: 91.0-100.0), with specificities ranging from 48.3% (95% CrI: 39.0-57.6) to 98.9% (95% CrI: 96.2-100.0). Sensitivity for IgG varied between 76.9% (95% CrI: 68.2-84.7) and 99.1% (95% CrI: 96.1-100.0), and specificity ranged from 49.9% (95% CrI: 19.4-95.8) to 99.3% (95% CrI: 97.2-100.0). LCM results were comparable to BLCM. Considering the RT-PCR as a gold standard underestimated the tests' sensitivity, particularly for IgM. Conclusion: BLCM-derived results deviated from those using a gold standard, which underestimated the tests' characteristics, particularly sensitivity. Although Bayesian and frequentist LCM approaches yielded comparable results, BLCM had the benefit of enabling credibility interval computation even when sample power is limited."
publishDate 2024
dc.date.none.fl_str_mv 2024
2024-01-01T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://hdl.handle.net/10216/157573
url https://hdl.handle.net/10216/157573
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 0895-4356
10.1016/j.jclinepi.2024.111267
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
repository.mail.fl_str_mv
_version_ 1799137791910608896