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

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 <i>across</i> trials (at the aggregate level) differs from that observed <i>within</i> 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 level, ideally using an individual participant data (IPD) meta-analysis. Here, we explain this issue and provide new intuition about how trial-level confounding is impacted by differences in within-trial distributions of covariates and how this corresponds to asymmetry in subgroup-specific funnel plots in the case of categorical covariates. We then propose a sensitivity analysis to assess the robustness of interaction estimates to potential trial-level confounding. We illustrate these concepts using simulated and real data from an IPD meta-analysis of trials conducted on the TICO/ACTIV-3 platform, which assessed passive immunotherapy treatments for inpatients with COVID-19.