Two‐phase Bayesian latent class analysis to assess diagnostic test performance in the absence of a gold standard: COVID‐19 serological assays as a proof of concept

Background and Objectives In this proof‐of‐concept study, which included blood donor samples, we aimed to demonstrate how Bayesian latent class models (BLCMs) could be used to estimate SARS‐CoV‐2 seroprevalence in the absence of a gold standard assay under a two‐phase sampling design. Materials and Methods To this end, 6810 plasma samples from blood donors who resided in Québec (Canada) were collected from May to July 2020 and tested for anti‐SARS‐CoV‐2 antibodies using seven serological assays (five commercial and two non‐commercial). Results SARS‐CoV‐2 seroprevalence was estimated at 0.71% (95% credible interval [CrI] = 0.53%–0.92%). The cPass assay had the lowest sensitivity estimate (88.7%; 95% CrI = 80.6%–94.7%), while the Héma‐Québec assay had the highest (98.7%; 95% CrI = 97.0%–99.6%). Conclusion The estimated low seroprevalence (which indicates a relatively limited spread of SARS‐CoV‐2 in Quebec) might change rapidly—and this tool, developed using blood donors, could enable a rapid update of the prevalence estimate in the absence of a gold standard. Further, the present analysis illustrates how a two‐stage BLCM sampling design, along with blood donor samples, can be used to estimate the performance of new diagnostic tests and inform public health decisions regarding a new or emerging disease for which a perfect reference standard does not exist.