Statistical methods for the analysis of adverse event data in randomised controlled trials: a scoping review and taxonomy

Abstract

Background: Statistical methods for the analysis of harm outcomes in randomised controlled trials (RCTs) are rarely used, and there is a reliance on simple approaches to display information such as in frequency tables. We aimed to identify whether any statistical methods had been specifically developed to analyse prespecified secondary harm outcomes and non-specific emerging adverse events (AEs).

Methods: A scoping review was undertaken to identify articles that proposed original methods or the original application of existing methods for the analysis of AEs that aimed to detect potential adverse drug reactions (ADRs) in phase II-IV parallel controlled group trials. Methods where harm outcomes were the (co)-primary outcome were excluded. Information was extracted on methodological characteristics such as: whether the method required the event to be prespecified or could be used to screen emerging events; and whether it was applied to individual events or the overall AE profile. Each statistical method was appraised and a taxonomy was developed for classification.

Results: Forty-four eligible articles proposing 73 individual methods were included. A taxonomy was developed and articles were categorised as: visual summary methods (8 articles proposing 20 methods); hypothesis testing methods (11 articles proposing 16 methods); estimation methods (15 articles proposing 24 methods); or methods that provide decision-making probabilities (10 articles proposing 13 methods). Methods were further classified according to whether they required a prespecified event (9 articles proposing 12 methods), or could be applied to emerging events (35 articles proposing 61 methods); and if they were (group) sequential methods (10 articles proposing 12 methods) or methods to perform final/one analyses (34 articles proposing 61 methods).

Conclusions: This review highlighted that a broad range of methods exist for AE analysis. Immediate implementation of some of these could lead to improved inference for AE data in RCTs. For example, a well-designed graphic can be an effective means to communicate complex AE data and methods appropriate for counts, time-to-event data and that avoid dichotomising continuous outcomes can improve efficiencies in analysis. Previous research has shown that adoption of such methods in the scientific press is limited and that strategies to support change are needed.

Trial registration: PROSPERO registration: https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=97442.

Keywords: Adverse events, harms, adverse drug reactions; Investigational drug; Methodological review; Randomised controlled trials; Scoping review; Signal detection.

Fig. 1

Flow diagram describing the assessment of sources of evidence

Fig. 2

Taxonomy of methods for adverse event (AE) analysis

Fig. 3

Classification terminology

Fig. 4

Volcano plot for adverse events experienced by at least three participants in either treatment group from Whone et al. The size of the circle represents the total number of participants with that event across treatment groups. Colour indicates direction of treatment effect. Colour saturation indicates the strength of statistical significance (calculated from whichever test the author has deemed appropriate). Circles are plotted against a measure of difference between treatment groups such as risk difference or odds ratio on the x-axis and p-values (with a transformation such as a log transformation) on the y-axis. Data taken from Whone et al. (2019) [67].