SWADA (Same Weighting Across Different Analyses) is a consistency
principle, not a statistical model parallel to da(), ad(), or cams().
da() (Difference of Averages) and ad() (Average Difference) are
different constructions (Panaro, Röver & Friede 2026) and can assign
studies different effective contributions.
SWADA requires the same study weights for the subgroup-mean analyses and
for the within-trial interaction, so that
\(\mu_B - \mu_A = \gamma\).
Usage
swada(
data,
reference,
modifier = NULL,
trial = "study",
subgroup = "level",
yi = "yi",
vi = "vi",
weights = c("interaction"),
method = "REML",
...
)Arguments
- data
A data frame with
trial,subgroup,yi, andvi.- reference
Reference subgroup label.
- modifier
Column of the two levels. Overrides
subgroupwhen set. The default column remains"subgroup".- trial, subgroup, yi, vi
Column names.
subgroupremains accepted.- weights
Weighting scheme. Currently
"interaction"(recommended).- method
Estimator for the interaction RE-MA (
"REML"default, or"ML").- ...
Passed to
ad()/metafor::rma()for the interaction fit.- engine
Computational backend. Currently
"metafor"only. Bayesian SWADA is not implemented; Bayesian inference does not add CAMS IF functionality.- approach
Inferential approach. SWADA is frequentist only;
"bayes"is an error.
Details
The default (weights = "interaction") is Interaction RE-weights SWADA
(Panaro, Röver & Friede 2026): take the ad() weights
\(w_j \propto 1/(v_{g j}+\hat\tau_g^2)\) and apply them to both
subgroup series. Under that common vector the DA interaction is
algebraically identical to AD: \(\sum_j w_j(y_{Bj}-y_{Aj})=\sum_j w_j g_j\).
The interaction estimate itself is the AD/rma of \(g_j\); SWADA does not
introduce a third interaction model.
References
Panaro RV, Röver C, Friede T (2026). Subgroup comparisons within and across studies in meta-analysis. Research Synthesis Methods (in press). arXiv:2508.15531. https://doi.org/10.48550/arXiv.2508.15531
