Estimates of sensitivity and specificity of serological tests for SARS-CoV-2 specific antibodies using a Bayesian latent class model approach

In this study, we aimed to assess how accurately serological tests can detect antibodies (IgM and IgG) specific for SARS-CoV-2, the virus that causes COVID-19. As there is no perfect standard for comparison, we applied a statistical analysis called Bayesian latent class models. This analysis allowed us to estimate the sensitivity (true positive rate) and specificity (true negative rate) of the tests for IgM and IgG. We performed three additional analyses to compare the results obtained through different methods, including an analysis that considers a previous molecular diagnosis the gold standard. To do so, we considered samples from persons with a confirmed SARS-CoV-2 infection (positive samples) and pre-pandemic samples (negative samples) stored in a biobank. Six different rapid serological tests and one laboratory assay were used. We found that test sensitivity and specificities varied widely. Bayesian-derived results differed from those considering a previous diagnosis as the reference standard, which underestimated the test sensitivity.
Source: Journal of Clinical Epidemiology - Category: Epidemiology Authors: Tags: Original Article Source Type: research