Identifying which individuals benefit most from particular treatments or other interventions underpins so-called personalised or stratified medicine. However, single trials are typically underpowered for exploring whether participant characteristics, such as age or disease severity, determine an individual’s response to treatment. A meta-analysis of multiple trials, particularly one where individual participant data (IPD) are available, provides greater power to investigate interactions between participant characteristics (covariates) and treatment effects. We use a published IPD meta-analysis to illustrate three broad approaches used for testing such interactions. Based on another systematic review of recently published IPD meta-analyses, we also show that all three approaches can be applied to aggregate data as well as IPD. We also summarise which methods of analysing and presenting interactions are in current use, and describe their advantages and disadvantages. We recommend that testing for interactions using within-trials information alone (the deft approach) becomes standard practice, alongside graphical presentation that directly visualises this.