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Individual participant data (IPD) meta-analyses of randomised trials areconsidered a reliable way to assess participant-level treatment effectmodifiers but may not make the best use of the available data. Traditionally,effect modifiers are explored one covariate at a time, which gives rise to thepossibility that evidence of treatment-covariate interaction may be due toconfounding from a different, related covariate. We aimed to evaluate currentpractice when estimating treatment-covariate interactions in IPD meta-analysis,specifically focusing on involvement of additional covariates in the models. Wereviewed 100 IPD meta-analyses of randomised trials, published between 2015 and2020, that assessed at least one treatment-covariate interaction. We identifiedfour approaches to handling additional covariates: (1) Single interaction model(unadjusted): No additional covariates included (57/100 studies); (2) Singleinteraction model (adjusted): Adjustment for the main effect of at least oneadditional covariate (35/100); (3) Multiple interactions model: Adjustment forat least one two-way interaction between treatment and an additional covariate(3/100); and (4) Three-way interaction model: Three-way interaction formedbetween treatment, the additional covariate and the potential effect modifier(5/100). IPD is not being utilised to its fullest extent. In an exemplardataset, we demonstrate how these approaches can lead to different conclusions.Researchers should adjust for additional covariates when estimatinginteractions in IPD meta-analysis providing they adjust their main effects,which is already widely recommended. Further, they should consider whether morecomplex approaches could provide better information on who might benefit mostfrom treatments, improving patient choice and treatment policy and practice.

Use of multiple covariates in assessing treatment-effect modifiers: A methodological review of individual participant data meta-analyses