Subgroup comparisons within and across studies in meta-analysis

TL;DR

This paper addresses inconsistencies in subgroup-specific meta-analysis caused by differing subgroup prevalences across studies, proposing a novel weighting method (SWADA) that improves bias and coverage in estimates, especially under imbalance.

Contribution

The paper introduces SWADA, a new weighting approach for meta-analysis that corrects compositional bias and enhances the accuracy of subgroup and interaction effect estimates.

Findings

SWADA reduces bias in subgroup effect estimates.

SWADA improves coverage probabilities in simulations.

Application to COVID-19 trials demonstrates practical benefits.

Abstract

Subgroup-specific meta-analysis synthesizes treatment effects for patient subgroups across randomized trials. Methods include joint or separate modeling of subgroup effects and treatment-by-subgroup interactions, but inconsistencies arise when subgroup prevalence differs between studies (e.g., proportion of non-smokers). A key distinction is between study-generated evidence within trials and synthesis-generated evidence obtained by contrasting results across trials. This distinction matters for identifying which subgroups benefit or are harmed most. Failing to separate these evidence types can bias estimates and obscure true subgroup-specific effects, leading to misleading conclusions about relative efficacy. Standard approaches often suffer from such inconsistencies, motivating alternatives. We investigate standard and novel estimators of subgroup and interaction effects in random-effects meta-analysis and study their properties. We show that using the same weights across different analyses (SWADA) resolves inconsistencies from unbalanced subgroup distributions and improves subgroup and interaction estimates. Analytical and simulation studies demonstrate that SWADA reduces bias and improves coverage, especially under pronounced imbalance. To illustrate, we revisit recent meta-analyses of randomized trials of COVID-19 therapies. Beyond COVID-19, the findings outline a general strategy for correcting compositional bias in evidence synthesis, with implications for decision-making and statistical modeling. We recommend the Interaction RE-weights SWADA as a practical default when aggregation bias is plausible: it ensures collapsibility, maintains nominal coverage with modest width penalty, and yields BLUE properties for the interaction.