Bayesian hierarchical models incorporating study-level covariates for multivariate meta-analysis of diagnostic tests without a gold standard with application to COVID-19
© 2023 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd..
When evaluating a diagnostic test, it is common that a gold standard may not be available. One example is the diagnosis of SARS-CoV-2 infection using saliva sampling or nasopharyngeal swabs. Without a gold standard, a pragmatic approach is to postulate a "reference standard," defined as positive if either test is positive, or negative if both are negative. However, this pragmatic approach may overestimate sensitivities because subjects infected with SARS-CoV-2 may still have double-negative test results even when both tests exhibit perfect specificity. To address this limitation, we propose a Bayesian hierarchical model for simultaneously estimating sensitivity, specificity, and disease prevalence in the absence of a gold standard. The proposed model allows adjusting for study-level covariates. We evaluate the model performance using an example based on a recently published meta-analysis on the diagnosis of SARS-CoV-2 infection and extensive simulations. Compared with the pragmatic reference standard approach, we demonstrate that the proposed Bayesian method provides a more accurate evaluation of prevalence, specificity, and sensitivity in a meta-analytic framework.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:42 |
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| Enthalten in: |
Statistics in medicine - 42(2023), 28 vom: 10. Dez., Seite 5085-5099 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Wang, Zheng [VerfasserIn] |
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| Links: |
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| Themen: |
Bayesian hierarchical model |
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| Anmerkungen: |
Date Completed 02.02.2024 Date Revised 03.02.2024 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1002/sim.9902 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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NLM362216029 |
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| 520 | |a When evaluating a diagnostic test, it is common that a gold standard may not be available. One example is the diagnosis of SARS-CoV-2 infection using saliva sampling or nasopharyngeal swabs. Without a gold standard, a pragmatic approach is to postulate a "reference standard," defined as positive if either test is positive, or negative if both are negative. However, this pragmatic approach may overestimate sensitivities because subjects infected with SARS-CoV-2 may still have double-negative test results even when both tests exhibit perfect specificity. To address this limitation, we propose a Bayesian hierarchical model for simultaneously estimating sensitivity, specificity, and disease prevalence in the absence of a gold standard. The proposed model allows adjusting for study-level covariates. We evaluate the model performance using an example based on a recently published meta-analysis on the diagnosis of SARS-CoV-2 infection and extensive simulations. Compared with the pragmatic reference standard approach, we demonstrate that the proposed Bayesian method provides a more accurate evaluation of prevalence, specificity, and sensitivity in a meta-analytic framework | ||
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| 700 | 1 | |a Xiao, Mengli |e verfasserin |4 aut | |
| 700 | 1 | |a Lin, Lifeng |e verfasserin |4 aut | |
| 700 | 1 | |a Alemayehu, Demissie |e verfasserin |4 aut | |
| 700 | 1 | |a Chu, Haitao |e verfasserin |4 aut | |
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