Analysis and reporting of adverse events in randomised controlled trials: a review

Objective: To ascertain contemporary approaches to the collection, reporting and analysis of adverse events (AEs) in randomised controlled trials (RCTs) with a primary efficacy outcome.

Design: A review of clinical trials of drug interventions from four high impact medical journals.

Data sources: Electronic contents table of the BMJ, the Journal of the American Medical Association (JAMA), the Lancet and the New England Journal of Medicine (NEJM) were searched for reports of original RCTs published between September 2015 and September 2016.

Methods: A prepiloted checklist was used and single data extraction was performed by three reviewers with independent check of a randomly sampled subset to verify quality. We extracted data on collection methods, assessment of severity and causality, reporting criteria, analysis methods and presentation of AE data.

Results: We identified 184 eligible reports (BMJ n=3; JAMA n=38, Lancet n=62 and NEJM n=81). Sixty-two per cent reported some form of spontaneous AE collection but only 29% included details of specific prompts used to ascertain AE data. Numbers that withdrew from the trial were well reported (80%), however only 35% of these reported whether withdrawals were due to AEs. Results presented and analysis performed was predominantly on 'patients with at least one event' with 84% of studies ignoring repeated events. Despite a lack of power to undertake formal hypothesis testing, 47% performed such tests for binary outcomes.

Conclusions: This review highlighted that the collection, reporting and analysis of AE data in clinical trials is inconsistent and RCTs as a source of safety data are underused. Areas to improve include reducing information loss when analysing at patient level and inappropriate practice of underpowered multiple hypothesis testing. Implementation of standard reporting practices could enable a more accurate synthesis of safety data and development of guidance for statistical methodology to assess causality of AEs could facilitate better statistical practice.