Examining covariate-specific treatment effects in individual participant data meta-analysis: Framing aggregation bias in terms of trial-level confounding and funnel plots.

ABSTRACT: To understand a treatment's potential impact at the individual level, it is crucial to explore whether the effect differs across patient subgroups and covariate values. Meta-analysis provides an important tool for detecting treatment-covariate interactions, as it can improve power compared to a single study. However, aggregation bias can occur when estimating individual-level treatment-covariate interactions in meta-analysis, due to trial-level confounding. This refers to when the association between the covariate and treatment effect across trials (at the aggregate level) differs from that observed within trials (at the individual level). It is, thus, recommended that heterogeneity in the treatment effect at the individual level should be disentangled from that at the trial

SUBMITTER: Siegel LK 

PROVIDER: S-EPMC12823212 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature