Evaluating COVID-19 Vaccine Efficacy Using Kaplan–Meier Survival Analysis - Hilal W; Chislett MG; Wu Y; Snider B; McBean EA; Yawney J; Gadsden SA (MDPI, 2024)
Analyses of COVID-19 vaccines have become a forefront of pandemic-related research, as jurisdictions around the world encourage vaccinations as the most assured method to curtail the need for stringent public health measures. Kaplan–Meier models, a form of “survival analysis”, provide a statistical approach to improve the understanding of time-to-event probabilities of occurrence. In applications of epidemiology and the study of vaccines, survival analyses can be implemented to quantify the probability of testing positive for SARS-CoV-2, given a population’s vaccination status. In this study, a large proportion of Ontario COVID-19 testing data is used to derive Kaplan–Meier probability curves for individuals who received two doses of a vaccine during a period of peak Delta variant cases, and again for those receiving three doses during a peak time of the Omicron variant. Data consisting of 614,470 individuals with two doses of a COVID-19 vaccine, and 49,551 individuals with three-doses of vaccine, show that recipients of the Moderna vaccine are slightly less likely to test positive for the virus in a 38-day period following their last vaccination than recipients of the Pfizer vaccine, although the difference between the two is marginal in most age groups. This result is largely consistent for two doses of the vaccines during a Delta variant period, as well as an Omicron variant period. The evaluated probabilities of testing positive align with the publicly reported vaccine efficacies of the mRNA vaccines, supporting the resolution that Kaplan–Meier methods in determining vaccine benefits are a justifiable and useful approach in addressing vaccine-related concerns in the COVID-19 landscape.
Keywords: 49 Mathematical Sciences; 31 Biological Sciences; 4905 Statistics; Vaccine Related; Immunization; Coronaviruses Vaccines; Infectious Diseases; Emerging Infectious Diseases; Coronaviruses Disparities and At-Risk Populations; Biotechnology; Prevention; Coronaviruses; 3.4 Vaccines; Infection; 3 Good Health and Well Being