Meta-analysis of Censored Adverse Events

TL;DR

This paper introduces a Bayesian meta-analysis method that effectively handles censored and rare adverse event data, improving accuracy in estimating drug safety across multiple studies.

Contribution

It proposes a novel Bayesian approach to incorporate censored adverse event data in meta-analyses, addressing a common but underexplored problem in drug safety assessment.

Findings

Improves accuracy of incidence rate estimates with censored data

Demonstrates effectiveness through simulation studies

Facilitates better-informed drug safety decisions

Abstract

Meta-analysis is a powerful tool for assessing drug safety by combining treatment-related toxicological findings across multiple studies, as clinical trials are typically underpowered for detecting adverse drug effects. However, incomplete reporting of adverse events (AEs) in published clinical studies is a frequent issue, especially if the observed number of AEs is below a pre-specified study-dependent threshold. Ignoring the censored AE information, often found in lower frequency, can significantly bias the estimated incidence rate of AEs. Despite its importance, this common meta-analysis problem has received little statistical or analytic attention in the literature. To address this challenge, we propose a Bayesian approach to accommodating the censored and possibly rare AEs for meta-analysis of safety data. Through simulation studies, we demonstrate that the proposed method can improves accuracy in point and interval estimation of incidence probabilities, particularly in the presence of censored data. Overall, the proposed method provides a practical solution that can facilitate better-informed decisions regarding drug safety.