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.

Abstract

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. 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). 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%). 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.