More Accurate Estimates of the Accuracy of RT-PCR and Chest CT Tests for COVID-19

In this article, we propose a novel statistical method for estimating the accuracy of chest computed tomography (CT) and reverse transcription polymerase chain reaction (RT-PCR) tests in the diagnosis of coronavirus disease 2019 (COVID-19), with a correction for imperfect gold standard and verification bias simultaneously. These two types of bias are often involved in estimating the diagnostic accuracy of COVID-19 tests. Imperfect gold standard bias arises when estimating accuracy measures of chest CT while using the RT-PCR test as a gold standard, despite its tendency to produce false negative results. Meanwhile, verification bias occurs in some studies where the results from chest CT are verified by RT-PCR test in a subsample of suspected cases that is not representative of the original population. Consequently, the accuracy estimates of chest CT and RT-PCR tests could be seriously biased and lead to invalid inference. Our proposed method is able to correct these two types of bias in providing unbiased and more accurate estimates of sensitivity and specificity of the two tests. Our results suggest that chest CT has higher sensitivity and lower specificity than RT-PCR, and the accuracy estimates can serve as an important reference for assessing and comparing the performance of these two tests in the diagnosis of COVID-19, and could guide policy recommendations for the implementation of these tests.

Recognising bias in studies of diagnostic tests part 2: interpreting and verifying the index test

Multiple pitfalls can occur with the conduct and analysis of a study of diagnostic tests, resulting in biased accuracy. Our conceptual model includes three stages: patient selection, interpretation of the index test and disease verification. In part 2, we focus on (1) Interpretation bias (or workup bias): where the classification of an indeterminate index test result can bias the accuracy of a test or how lack of blinding can bias a subjective test result, and (2) Disease verification bias: where the index test result is incorporated into the gold standard or when the gold standard is applied only to a select population as the gold standard is an invasive test. In an example with age-adjusted D-dimer for pulmonary embolism, differential verification bias was a limitation due to the use of two gold standards—CT for a high-risk population and follow-up for symptoms in a low-risk population. However, there are circumstances when certain choices in study design are unavoidable, and result in biased test characteristics. In this case, the informed reader will better judge the quality of a study by recognising the potential biases and limitations by being methodical in their approach to understanding the methods, and in turn, better apply studies of diagnostic tests into their clinical practice.