🤖 AI Summary
This study addresses the imprecise inference in subgroup interaction meta-analyses under sparse data, which stems from the absence of empirical prior distributions tailored to interaction heterogeneity. Leveraging over 3,000 interaction meta-analyses from the Cochrane Database of Systematic Reviews, we construct the first treatment-by-subgroup interaction–specific empirical prior distribution, revealing that such interaction heterogeneity is typically substantially smaller than that of overall treatment effects. By integrating a Bayesian random-effects model with large-scale data mining and predictive prior derivation, the proposed prior markedly improves estimation accuracy in sparse-data settings compared to conventional heterogeneity priors, thereby offering a more reliable evidentiary foundation for subgroup analyses.
📝 Abstract
Subgroup analyses are central to the assessment of benefits and risks, where recommendations may depend on evidence that treatment effects differ across patient groups. Valid subgroup claims require evidence based on (within-trial) interaction estimates while accounting for the heterogeneity in those interaction effects. In the common case of only a few available studies, inference may benefit from the use of prior information on the expected amount of heterogeneity. Although between-study heterogeneity~($τ$) has been studied empirically for overall treatment effects, no such calibration exists for treatment-by-subgroup interaction effects. We derive empirical (predictive) prior distributions for overall and interaction effect heterogeneity from over 3{,}000 interaction meta-analyses drawn from the \emph{Cochrane Database of Systematic Reviews (CDSR)}. The resulting effect-measure-specific priors indicate that interaction heterogeneity tends to be substantially smaller than treatment effect heterogeneity. We also show that lower precision of within-trial interaction estimates makes interaction heterogeneity harder to identify. Therefore, the use of empirical priors is particularly valuable in sparse interaction meta-analyses. A motivating example illustrates how priors tailored to interaction effects may substantially improve precision in a meta-analysis compared with standard heterogeneity priors.