Meta-analysis of Censored Adverse Events

Authors

Xinyue Qi
Shouhao Zhou
Christine B Peterson
Yucai Wang
Xinying Fang
Michael L Wang
Chan Shen

Publication Date

10-1-2024

Journal

The New England Journal of Statistics in Data Science

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 frequently encountered, 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 prevalent issue in meta-analysis 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 improve 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.

Keywords

Adverse drug reaction, Bayesian inference, Drug safety, Incomplete reporting, MAGEC, Meta-analysis

DOI

10.51387/24-nejsds62

PMID

39991459

PMCID

PMC11845246

PubMedCentral® Posted Date

2-21-2025

PubMedCentral® Full Text Version

Author MSS

Published Open-Access

yes