Abstract With the emergence and spread of infectious diseases with pandemic potential, such as COVID-19, in a relatively short time, the leading pharmaceutical companies*, received an Emergency Use Authorization (EUA) for vaccine’s en-mass deployment. To monitor for potential acute side effect(s) of the vaccine during the (initial) vaccination campaign, we developed an optimal sequential test that allows for an early detection of potential acute side effect(s). This test employs a rule to stop the vaccination process once the observed number of side effect(s) incidents exceeds a certain (pre-determined) threshold. In the case of a single side effect, we study the properties of the sequential test and derive the exact expressions of the Average Sample Number (ASN) curve of the stopping time (and its variance) via the regularized incomplete beta function. Additionally, we derive the asymptotic behavior of the relative ’savings’ in ASN as compared to maximal sample size. Moreover, we construct the post-test parameter estimate and study its sampling properties, including its asymptotic behavior under local-type alternatives. These limiting behavior results provide the consistency and asymptotic normality of the post-test parameter estimator. The results of a small simulation study are provided along with a detailed example based on COVID-19 side effect data.
Wang et al. (Fri,) studied this question.