Shiny-MAGEC: A Bayesian R shiny application for meta-analysis of censored adverse events
Zihan Zhou, Zizhong Tian, Christine Peterson, Le Bao, Shouhao Zhou
TL;DR
This paper introduces Shiny-MAGEC, a user-friendly tool that improves the accuracy of drug safety assessments by analyzing incomplete adverse event data using Bayesian methods.
Contribution
A novel R Shiny application for Bayesian meta-analysis of censored adverse events in clinical trials.
Findings
Shiny-MAGEC enables unbiased estimation of adverse event incidence by incorporating censored data.
The tool allows direct comparison between models that account for or ignore censoring, revealing potential biases.
An illustrative example on PD-1/PD-L1 inhibitors demonstrates the app's utility in safety evaluations.
Abstract
Accurate assessment of adverse event (AE) incidence is critical in clinical research for drug safety. While meta-analysis serves as an essential tool to comprehensively synthesize the evidence across multiple studies, incomplete AE reporting in clinical trials remains a persistent challenge. In particular, AEs occurring below study-specific reporting thresholds are often omitted from publications, leading to left-censored data. Failure to account for these censored AE counts can result in biased AE incidence estimates. We present an R Shiny application that implements a Bayesian meta-analysis model specifically designed to incorporate censored AE data into the estimation process. This interactive tool provides a user-friendly interface for researchers to conduct AE meta-analyses and estimate the AE incidence probability using an unbiased approach. It also enables direct comparisons…
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Taxonomy
TopicsStatistical Methods in Clinical Trials · Meta-analysis and systematic reviews · Pharmacovigilance and Adverse Drug Reactions