Safety evaluation of new therapies is an essential aspect of clinical trials, primarily quantifying the incidence of adverse events (AEs) and comparing it to a standard treatment. Despite its importance, safety analysis of adverse events is often rather simplistic: AEs probabilities are estimated without explicitly defining the target causal comparison and neglecting assumptions on the censoring mechanisms, leading to differential follow-up times. In this work, we make a first proposal addressing the evaluation of drugs’ safety in the estimand strategy framework under the Principal Stratification approach. We define principal estimands and estimate them under the assumption of principal ignorability leveraging a fully Bayesian model.