Interpreting COVID-19 Test Results: A Bayesian Approach
2020
This article considers the following question with respect to interpreting the results of polymerase chain reaction (PCR) assays from nasal and pharyngeal swabs for COVID-19 to inform clinical decision making: "While a positive result in an acutely ill patient is straightforward, how should physicians interpret negative tests in patients with suspected COVID-19 infection?"
Using an assumption of near-perfect specificity of PCR assays for COVID-19, the authors acknowledge the uncertainty of test sensitivity. They consider two clinical scenarios with different contact history and clinical presentations. Using a Bayesian approach and example calculations, they illustrate the influence of the prior probability (prevalence), the probability of a positive test given disease (sensitivity) and the probability of a negative test given no disease (specificity) on the post-test probability of COVID-19. In particular, the scenarios they choose illustrate the influence of the prior (prevalence) on the post-test probability of disease - the clinically relevant conditional probability for decision making.
Interpreting COVID-19 Test Results: A Bayesian Approach
Source:
Good CB, Hernandez I, Smith K. Interpreting COVID-19 Test Results: A Bayesian Approach. Journal of General Internal Medicine 2020; 35: 2790-2491. https://doi.org/10.1007/s11606-020-05918-8