Bayesian hierarchical models incorporating study-level covariates for multivariate meta-analysis of diagnostic tests without a gold standard with application to COVID-19.

Wang, Zheng; Murray, Thomas A; Xiao, Mengli; Lin, Lifeng; Alemayehu, Demissie; Chu, Haitao

Wang, Zheng; Murray, Thomas A; Xiao, Mengli; Lin, Lifeng; Alemayehu, Demissie; Chu, Haitao.

Afiliação

Wang Z; Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, Minnesota, USA.
Murray TA; Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, Minnesota, USA.
Xiao M; Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA.
Lin L; Department of Epidemiology and Biostatistics, University of Arizona, Tucson, Arizona, USA.
Alemayehu D; Global Biometrics and Data Management, Pfizer Inc., New York, New York, USA.
Chu H; Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, Minnesota, USA.

Stat Med ; 42(28): 5085-5099, 2023 Dec 10.

Article em En | MEDLINE | ID: mdl-37724773

Assuntos

COVID-19; Humanos; COVID-19/diagnóstico; COVID-19/epidemiologia; SARS-CoV-2; Teorema de Bayes; Sensibilidade e Especificidade; Testes Diagnósticos de Rotina/métodos; Teste para COVID-19

Palavras-chave

Bayesian hierarchical model; SARS-CoV-2 infection diagnosis; diagnostic test; double negatives; meta-analysis; sensitivity; specificity

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Texto completo: 1 Base de dados: MEDLINE Assunto principal: COVID-19 Tipo de estudo: Diagnostic_studies / Risk_factors_studies / Systematic_reviews Limite: Humans Idioma: En Revista: Stat Med Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Estados Unidos

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