Measuring Vaccine Efficacy Against Infection and Disease in Clinical Trials: Sources and Magnitude of Bias in Coronavirus Disease 2019 (COVID-19) Vaccine Efficacy Estimates. (26th October 2021)

• Record Type: Journal Article
• Authors: Williams, Lucy R; Ferguson, Neil M; Donnelly, Christl A; Grassly, Nicholas C
• Is Part Of: Clinical infectious diseases. Volume 75:Number 1(2022)
• Page Start: e764
• Page End: e773
• Publication Date: 2021-10-26
• Subjects: COVID-19 -- SARS-CoV-2 -- vaccine efficacy -- bias -- Clinical Trial; Communicable diseases -- Periodicals; 616.905
• DOI: 10.1093/cid/ciab914
• Languages: English
• ISSNs: 1058-4838

Abstract

Abstract: Background: Phase III trials have estimated coronavirus disease 2019 (COVID-19) vaccine efficacy (VE) against symptomatic and asymptomatic infection. We explore the direction and magnitude of potential biases in these estimates and their implications for vaccine protection against infection and against disease in breakthrough infections. Methods: We developed a mathematical model that accounts for natural and vaccine-induced immunity, changes in serostatus, and imperfect sensitivity and specificity of tests for infection and antibodies. We estimated expected biases in VE against symptomatic, asymptomatic, and any severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections and against disease following infection for a range of vaccine characteristics and measurement approaches, and the likely overall biases for published trial results that included asymptomatic infections. Results: VE against asymptomatic infection measured by polymerase chain reaction (PCR) or serology is expected to be low or negative for vaccines that prevent disease but not infection. VE against any infection is overestimated when asymptomatic infections are less likely to be detected than symptomatic infections and the vaccine protects against symptom development. A competing bias toward underestimation arises for estimates based on tests with imperfect specificity, especially when testing is performed frequently. Our model indicates considerable uncertainty in Oxford-AstraZeneca ChAdOx1 and Janssen Ad26.COV2.S VE against any infection, with slightly higher than published, bias-adjusted values of 59.0% (95% uncertainty interval [UI] 38.4–77.1) and 70.9% (95% UI 49.8–80.7), respectively. Conclusions: Multiple biases are likely to influence COVID-19 VE estimates, potentially explaining the observed difference between ChAdOx1 and Ad26.COV2.S vaccines. These biases should be considered when interpreting both efficacy and effectiveness study results. Abstract : Multiple biases have the potential to influence vaccine efficacy estimates, with their direction and magnitude dependent on the vaccine properties and testing strategies. We expect vaccine efficacy against infection to be slightly underestimated in the coronavirus disease 2019 vaccine trials.