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

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

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish (US)
Pages (from-to)5085-5099
Number of pages15
JournalStatistics in Medicine
Volume42
Issue number28
DOIs
StatePublished - Dec 10 2023

Bibliographical note

Publisher Copyright:
© 2023 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.

Keywords

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

PubMed: MeSH publication types

  • Meta-Analysis
  • Journal Article

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