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.