key: cord-0829880-oio2e6oy
authors: Hartnack, Sonja; Eusebi, Paolo; Kostoulas, Polychronis
title: Bayesian latent class models to estimate diagnostic test accuracies of COVID‐19 tests
date: 2020-08-08
journal: J Med Virol
DOI: 10.1002/jmv.26405
sha: fd28e3216d96e17be5a0caa20c24736c6116c572
doc_id: 829880
cord_uid: oio2e6oy

With great interest we read the article from Dramé et al. 1 “Should RT‐PC be considered as gold standard in the diagnosis of Covid‐19” questioning the conclusions from Cassaniti et al.(2). We agree with the argument that considering RT‐PCR as gold standard when evaluating a new test will inevitably lead to biased test accuracy estimates of the new test(1). This article is protected by copyright. All rights reserved.

estimated from the data. BLCMs have been applied widely, reporting guidelines have been developed 3 , and currently an EU-funded initiative aims to expand their application in the evaluation of diagnostic tests (www.harmony-net.eu).

For show-casing BLCMs we used the data from Cassaniti et al. 2 , although the keys to validity in diagnostic test studies are not respected, i.e. "a consecutive series of patients suspected (but not known) to have the target disorder" 4 -which precludes the generalisability of the findings. We considered a model with three tests for the diagnosis of COVID-19 in one population (110 patients), and extracted the following data of cross-classified test results (RT-PCR, IgG and IgM) 2 : +++ 28, ++-1, +-+ 3, +--36, -++ 0, -+-0, --+ 1, ---41. We aim to estimate the prevalence, the sensitivities and the specificities of three tests, and potential conditional dependencies (correlations) between tests. Since the maximum number of estimable parameters for three tests in one population is seven 5 , this model is not identifiable. Therefore, in a Bayesian framework, we rely on prior information for the specificities of all three tests similar to a previous analysis with two tests and four populations 6 . We assume that they are close to 100%. Further technical details about the model and Markov Chain Monte Carlo simulation, including the code to reproduce the results, are available on This article is protected by copyright. All rights reserved.

(https://github.com/shartn/BLCM-COVID19). In Table 1 the posteriors resulting from   a model with conditional dependency between the IgG and IgM test sensitivities are displayed.

In contrast to the previous analysis 2 , which used RT-PCR as a gold standard, implicitly assuming 100% sensitivity and specificity, the RT-PCR sensitivity based on our BLCM analysis is lower, while the specificity of all three tests is close to 100%. The estimated sensitivity of the combined IgM/IgG in the study from Cassaniti et al. 2 was 18.4%, being outside the 95% probability interval of our BLCM estimates for both the IgM and IgG. Although, based on our BLCM analysis, we cannot state "that the new test might be better" 1 , we are confident that BLCMs allow estimation of sensitivities and specificities in an unbiased way without needing a gold standard. Since this data setwith the exception of the 50 emergency patients -does not comply with the key aspects of validity 4 , it is not possible to generalise the results of our BLCM beyond the study population of the 110 patients.

Despite enormous COVID-19 research activities, the application of BLCMs and suitable data sets are virtually absent. For the preparedness of future pandemics, we suggest to include the design of appropriate diagnostic test accuracy studies, which will also be suitable for BLCMs. 

Should RT-PCR be considered a gold standard in the diagnosis of Covid-19?

Rapid Test is inadequate for diagnosis of COVID-19 in acute patients referring to emergency room department

STARD-BLCM: Standards for the Reporting of Diagnostic accuracy studies that use Bayesian Latent Class Models

The architecture of diagnostic research

Estimating disease prevalence in a Bayesian framework using probabilistic constraints

Diagnostic accuracy estimates for COVID-19

Lateral flow immunoassay tests with Bayesian latent class models