Abstract
Ideally, the sequential monitoring of adverse events following post-licensed drugs and vaccines is correctly adjusted for confounding variables, such as gender and age, that may have an effect on the quality of the events. This is the idea behind the usual fully randomized, the placebo-control, and the self-control designs. Two prominent methods for conducting sequential analysis of the safety of post-market drugs and vaccines are the maximized sequential probability ratio test (MaxSPRT), and its conditional version, the CMaxSPRT. However, even when the assumption of sample homogeneity is realistic prior to the drug/vaccine administration, the effects caused by the drugs and vaccines on the risk of an adverse event, if any, can still vary according to observable covariates. For binomial and Poisson data, a straightforward sequential test method is introduced in order to accommodate a regression structure in the MaxSPRT. The proposed sequential regression test is also applicable for the CMaxSPRT, that is, the regression works for comparing historical and surveillance Poisson data with unknown heterogeneous baseline rates, taking into account seasonality and any other observable confounding covariates. To illustrate the usefulness of such a regression method, we describe the potential applications of the method to monitor vaccine-adverse events in Manitoba, Canada. The numeric results and examples were executed with the R Sequential package.