Improving the analysis of adverse event data in randomized controlled trials

Abstract

Analyzing treatment harm is vital but problematic with the relatively small sample sizes afforded in randomized controlled trials (RCTs). Good analysis practice for efficacy outcomes are well established but there has been minimal progress for the analysis of adverse events (AEs). In this commentary we examine four key issues for AE analysis. Namely, why harm data in RCTs is undervalued, why AE analysis is difficult, what aspects of current analysis practice are unsatisfactory, and the challenges for selection and interpretation of AEs results in publications. We discuss how the value of harm data from RCTs could be better realized by reframing the research question to one for detecting signals of adverse reactions. We align established good statistical practice to current unsatisfactory practice. We encourage use of Bayesian analyses to enable cumulative assessment of harm across trial research phases and discourage selecting AEs to report based on arbitrary rules. We propose comprehendible summaries to be based on core outcome sets, serious and pre-specified events, and events leading to discontinuation. Analysis of AEs in contemporary clinical trials needs attention to progress. In the following we have outlined immediate, mid and longer-term strategies for trialists to adopt to support a change in practice