  Meta-analysis is a powerful tool for drug safety assessment by synthesizing
treatment-related toxicological findings from independent clinical trials.
However, published clinical studies may or may not report all adverse events
(AEs) if the observed number of AEs were fewer than a pre-specified
study-dependent cutoff. Subsequently, with censored information ignored, the
estimated incidence rate of AEs could be significantly biased. To address this
non-ignorable missing data problem in meta-analysis, we propose a Bayesian
multilevel regression model to accommodate the censored rare event data. The
performance of the proposed Bayesian model of censored data compared to other
existing methods is demonstrated through simulation studies under various
censoring scenarios. Finally, the proposed approach is illustrated using data
from a recent meta-analysis of 125 clinical trials involving PD-1/PD-L1
inhibitors with respect to their toxicity profiles.
