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A Bayesian method for synthesizing multiple diagnostic outcomes of COVID-19 tests.
Cao, Lirong; Zhao, Shi; Li, Qi; Ling, Lowell; Wu, William K K; Zhang, Lin; Lou, Jingzhi; Chong, Marc K C; Chen, Zigui; Wong, Eliza L Y; Zee, Benny C Y; Chan, Matthew T V; Chan, Paul K S; Wang, Maggie H.
  • Cao L; JC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong SAR, People's Republic of China.
  • Zhao S; Clinical Trials and Biostatistics Lab, CUHK Shenzhen Research Institute, Shenzhen, People's Republic of China.
  • Li Q; JC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong SAR, People's Republic of China.
  • Ling L; Clinical Trials and Biostatistics Lab, CUHK Shenzhen Research Institute, Shenzhen, People's Republic of China.
  • Wu WKK; JC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong SAR, People's Republic of China.
  • Zhang L; Clinical Trials and Biostatistics Lab, CUHK Shenzhen Research Institute, Shenzhen, People's Republic of China.
  • Lou J; Department of Anaesthesia and Intensive Care, The Chinese University of Hong Kong, Hong Kong SAR, People's Republic of China.
  • Chong MKC; Department of Anaesthesia and Intensive Care, The Chinese University of Hong Kong, Hong Kong SAR, People's Republic of China.
  • Chen Z; Department of Anaesthesia and Intensive Care, The Chinese University of Hong Kong, Hong Kong SAR, People's Republic of China.
  • Wong ELY; JC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong SAR, People's Republic of China.
  • Zee BCY; JC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong SAR, People's Republic of China.
  • Chan MTV; Clinical Trials and Biostatistics Lab, CUHK Shenzhen Research Institute, Shenzhen, People's Republic of China.
  • Chan PKS; Department of Anaesthesia and Intensive Care, The Chinese University of Hong Kong, Hong Kong SAR, People's Republic of China.
  • Wang MH; JC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong SAR, People's Republic of China.
R Soc Open Sci ; 8(9): 201867, 2021 Sep.
Article Dans Anglais | MEDLINE | ID: covidwho-1429382
ABSTRACT
The novel coronavirus disease 2019 (COVID-19) has spread worldwide and threatened human life. Diagnosis is crucial to contain the spread of SARS-CoV-2 infections and save lives. Diagnostic tests for COVID-19 have varying sensitivity and specificity, and the false-negative results would have substantial consequences to patient treatment and pandemic control. To detect all suspected infections, multiple testing is widely used. However, it may be challenging to build an assertion when the testing results are inconsistent. Considering the situation where there is more than one diagnostic outcome for each subject, we proposed a Bayesian probabilistic framework based on the sensitivity and specificity of each diagnostic method to synthesize a posterior probability of being infected by SARS-CoV-2. We demonstrated that the synthesized posterior outcome outperformed each individual testing outcome. A user-friendly web application was developed to implement our analytic framework with free access via http//www2.ccrb.cuhk.edu.hk/statgene/COVID_19/. The web application enables the real-time display of the integrated outcome incorporating two or more tests and calculated based on Bayesian posterior probability. A simulation-based assessment demonstrated higher accuracy and precision of the Bayesian probabilistic model compared with a single-test outcome. The online tool developed in this study can assist physicians in making clinical evaluations by effectively integrating multiple COVID-19 tests.
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Texte intégral: Disponible Collection: Bases de données internationales Base de données: MEDLINE Type d'étude: Étude diagnostique / Études expérimentales / Étude pronostique langue: Anglais Revue: R Soc Open Sci Année: 2021 Type de document: Article

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Texte intégral: Disponible Collection: Bases de données internationales Base de données: MEDLINE Type d'étude: Étude diagnostique / Études expérimentales / Étude pronostique langue: Anglais Revue: R Soc Open Sci Année: 2021 Type de document: Article