Meta-analyses of published subgroup-level data may yield different interaction estimates depending on how the available data are weighted across studies. In particular, the difference between meta-analytic subgroup-specific effects need not equal the meta-analysis of within-study subgroup differences when these quantities are based on different study weights. Variation in subgroup prevalence across trials, including the presence of single-subgroup trials, may further amplify this discrepancy and introduce aggregation bias into across-study subgroup comparisons. We examine established approaches for jointly or separately estimating subgroup-specific effects and treatment-by-subgroup interactions and introduce the Same Weighting Across Different Analyses (SWADA) principle. SWADA applies a common set of study weights across the connected subgroup and interaction analyses, thereby guaranteeing algebraic agreement between the difference of subgroup averages and the average subgroup difference. Through simulation studies and meta-analyses of randomised trials of COVID-19 treatments, we compare several SWADA weighting schemes with existing approaches in terms of bias, coverage, precision, and root mean squared error. SWADA ensures internally coherent subgroup and interaction estimates, but may require down-weighting or excluding studies that provide information for only one subgroup. The choice of weights therefore involves a trade- off between algebraic coherence, use of the available subgroup evidence, and statistical efficiency. SWADA should be viewed as a general weighting principle for meta-analytic comparisons rather than as a universally preferable estimator.

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