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

Objectives: Assessing the accuracy of serological tests for SARS-CoV-2 was challenging due to the lack of a gold standard. This study aimed to estimate the accuracy of SARS-CoV-2-specific serological tests using Bayesian latent class models (BLCM) and compare methods with and without a gold standard.

Study Design And Setting: In this study, we analyzed 356 samples-254 positives, ie, from individuals with a previous SARS-CoV-2 infection diagnosis, and 102 negatives, ie, prepandemic samples-using six different rapid serological tests and one laboratory assay. A BLCM was employed to concurrently estimate the sensitivity and specificity of all serological tests for the immunoglobulin (Ig) M and IgG antibodies specific for SARS-CoV-2. Noninformative priors were used. A sensitivity analysis was conducted considering three methods: 1) reverse transcription-polymerase chain reaction test (RT-PCR) as the gold standard, 2) BLCM with RT-PCR as an imperfect gold standard, and 3) frequentist latent class model (LCM). All analyses used software R version 4.3.0, and BLCM were fitted using package runjags using the software JAGS (Just Another Gibbs Sampler).

Results: The BLCM-derived sensitivity for IgM varied from 10.7% [95% credibility interval (CrI):1.9-24.6] to 96.9% (95% CrI: 91.0-100.0), with specificities ranging from 48.3% (95% CrI: 39.0-57.6) to 98.9% (95% CrI: 96.2-100.0). Sensitivity for IgG varied between 76.9% (95% CrI: 68.2-84.7) and 99.1% (95% CrI: 96.1-100.0), and specificity ranged from 49.9% (95% CrI: 19.4-95.8) to 99.3% (95% CrI: 97.2-100.0). LCM results were comparable to BLCM. Considering the RT-PCR as a gold standard underestimated the tests' sensitivity, particularly for IgM.

Conclusion: BLCM-derived results deviated from those using a gold standard, which underestimated the tests' characteristics, particularly sensitivity. Although Bayesian and frequentist LCM approaches yielded comparable results, BLCM had the benefit of enabling credibility interval computation even when sample power is limited.