Low incidence rate of COVID-19 undermines confidence in estimation of the vaccine efficacy
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Knowing the true effect size of clinical interventions in randomised clinical trials is key to informing the public health policies. Vaccine efficacy is defined in terms of the relative risk or the ratio of two disease risks. However, only approximate methods are available for estimating the variance of the relative risk. In this article, we show using a probabilistic model that uncertainty in the efficacy rate could be underestimated when the disease risk is low. Factoring in the baseline rate of the disease, we estimate broader confidence intervals for the efficacy rates of the vaccines recently developed for COVID-19. We propose new confidence intervals for the relative risk. We further show that sample sizes required for phase 3 efficacy trials are routinely underestimated and propose a new method for sample size calculation where the efficacy is of interest. We also discuss the deleterious effects of classification bias which is particularly relevant at low disease prevalence.
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