Estimating interactions and subgroup‐specific treatment effects in meta‐analysis without aggregation bias: A within‐trial framework

Estimation of within‐trial interactions in meta‐analysis is crucial for reliable assessment of how treatment effects vary across participant subgroups. However, current methods have various limitations. Patients, clinicians and policy‐makers need reliable estimates of treatment effects within specific covariate subgroups, on relative and absolute scales, in order to target treatments appropriately—which estimation of an interaction effect does not in itself provide. Also, the focus has been on covariates with only two subgroups, and may exclude relevant data if only a single subgroup is reported. Therefore, in this article we further develop the “within‐trial” framework by providing practical methods to (1) estimate within‐trial interactions across two or more subgroups; (2) estimate subgroup‐specific (“floating”) treatment effects that are compatible with the within‐trial interactions and make maximum use of available data; and (3) clearly present this data using novel implementation of forest plots. We described the steps involved and apply the methods to two examples taken from previously published meta‐analyses, and demonstrate a straightforward implementation in Stata based upon existing code for multivariate meta‐analysis. We discuss how the within‐trial framework and plots can be utilised with aggregate (or “published”) source data, as well as with individual participant data, to effectively demonstrate how treatment effects differ across participant subgroups.