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Abstract
The authors evaluated the use of logistic regression to model the probabilities of spontaneously reported vaccine -- event pairs being adverse reactions following immunization (ARFI), using disproportionality and unexpectedness of time-to-onset (TTO) distributions as predictive variables and the presence of events in the global product information as a dependent variable. They used spontaneous reports of adverse events from eight vaccines and their labels as proxies for ARFIs. Model 3, using two quantified causality criteria, provided the best performance for all measures. The p value of the 'between vaccines' KS test was the most significant predictive factor. Model 1 had the worst performance. Logistic regression allows estimation of the probability of a vaccine -- event pair being an ARFI using two causality criteria at the population level assessed in spontaneous report data: the strength of association (disproportionality measure) and temporality (TTO distribution tests). Logistic regression combines and weights these causality criteria based on their respective ability to predict known safety issues.