Researchers develop method for evaluating long-term COVID-19 vaccine efficacy
April 27, 2021
The large-scale deployment of effective vaccines is globally recognized as the best way to end the COVID-19 pandemic. However, the high efficacy reported for vaccines currently in use — like Pfizer and Moderna — is based on an average follow-up time of only about two months after the second dose. The question remains: Will people need booster vaccinations?
As large-scale phase 3 placebo-controlled clinical trials for these and other vaccines continue, it would be ideal to both evaluate how long protection lasts while allowing trial participants timely access to highly effective vaccine. In a paper published March 10 in Clinical Infectious Diseases, researchers show how investigators can sequentially cross participants over from the placebo arm to the vaccine arm according to priority groups.
The paper’s lead author is Danyu Lin, PhD, Dennis Gillings Distinguished Professor in the Department of Biostatistics at the UNC Gillings School of Global Public Health. His co-authors are Donglin Zeng, PhD, professor of biostatistics at the Gillings School, and Peter B. Gilbert, PhD, professor in the Vaccine and Infectious Disease Division of the Fred Hutch and lead statistician with the COVID-19 Prevention Network, which conducts phase 3 efficacy trials for COVID-19 vaccines and monoclonal antibodies.
Titled, “Evaluating the Long-Term Efficacy of COVID-19 Vaccines,” their method-focused article demonstrates how to estimate time-varying vaccine efficacy (VE) through staggered vaccination of participants. They also compare the performance of blinded and unblinded crossover designs in estimating long-term VE.
“Assessing the durability of COVID-19 vaccines is a very hot topic. The knowledge about the potentially time-varying VE can be used to determine when booster vaccination is needed to sustain protection; this information is also an important input parameter in mathematical modeling of the population impact of COVID-19 vaccines,” said Lin. “My co-author, Peter, plans to use the methods described in this paper for a new trial in Africa, and other statisticians, including those from Pfizer and Johnson & Johnson, plan to apply our methods to their ongoing trials. While it takes time for any new statistical method to be used in an actual study, we are hopeful this one will make a real difference.”
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