Estimating vaccine efficacy during open-label follow-up of COVID-19 vaccine trials based on population-level surveillance data

• Published in: Epidemics Vol. 47; p. 100768
• Main Authors: Moore, Mia, Zhu, Yifan, Hirsch, Ian, White, Tom, Reiner, Robert C., Barber, Ryan M., Pigott, David, Collins, James K., Santoni, Serena, Sobieszczyk, Magdalena E., Janes, Holly
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.06.2024; Elsevier
• Subjects: Adult; Aged; Clinical trial design; Counterfactual; COVID-19 - epidemiology; COVID-19 - prevention & control; COVID-19 Vaccines - administration & dosage; COVID-19 Vaccines - therapeutic use; Female; Follow-Up Studies; Humans; Incidence; Male; Middle Aged; Population Surveillance - methods; Randomized controlled trial; SARS-CoV-2 - immunology; United States - epidemiology; Vaccine; Vaccine Efficacy - statistics & numerical data; United States; Counterfactual; Vaccine; Clinical trial design; Randomized controlled trial
• ISSN: 1755-4365; 1878-0067; 1878-0067
• DOI: 10.1016/j.epidem.2024.100768

While rapid development and roll out of COVID-19 vaccines is necessary in a pandemic, the process limits the ability of clinical trials to assess longer-term vaccine efficacy. We leveraged COVID-19 surveillance data in the U.S. to evaluate vaccine efficacy in U.S. Government-funded COVID-19 vaccine efficacy trials with a three-step estimation process. First, we used a compartmental epidemiological model informed by county-level surveillance data, a “population model”, to estimate SARS-CoV-2 incidence among the unvaccinated. Second, a “cohort model” was used to adjust the population SARS-CoV-2 incidence to the vaccine trial cohort, taking into account individual participant characteristics and the difference between SARS-CoV-2 infection and COVID-19 disease. Third, we fit a regression model estimating the offset between the cohort-model-based COVID-19 incidence in the unvaccinated with the placebo-group COVID-19 incidence in the trial during blinded follow-up. Counterfactual placebo COVID-19 incidence was estimated during open-label follow-up by adjusting the cohort-model-based incidence rate by the estimated offset. Vaccine efficacy during open-label follow-up was estimated by contrasting the vaccine group COVID-19 incidence with the counterfactual placebo COVID-19 incidence. We documented good performance of the methodology in a simulation study. We also applied the methodology to estimate vaccine efficacy for the two-dose AZD1222 COVID-19 vaccine using data from the phase 3 U.S. trial (ClinicalTrials.gov # NCT04516746). We estimated AZD1222 vaccine efficacy of 59.1% (95% uncertainty interval (UI): 40.4%–74.3%) in April, 2021 (mean 106 days post-second dose), which reduced to 35.7% (95% UI: 15.0%–51.7%) in July, 2021 (mean 198 days post-second-dose). We developed and evaluated a methodology for estimating longer-term vaccine efficacy. This methodology could be applied to estimating counterfactual placebo incidence for future placebo-controlled vaccine efficacy trials of emerging pathogens with early termination of blinded follow-up, to active-controlled or uncontrolled COVID-19 vaccine efficacy trials, and to other clinical endpoints influenced by vaccination. •By necessity, many clinical trials lack placebo groups.•External data can be used to simulate a placebo incidence.•COVID-19 incidence changed rapidly, necessitating mathematical modeling.•Simulated placebos can evaluate vaccine efficacy over a longer time window.