Bundle of DA and AD with analysis-specific weights

Runs da() and ad() on the same binary subgroup data. These are different constructions (Panaro, Röver & Friede 2026). Inverse-variance weights generally differ across the two subgroup series and the within-trial interaction, so the DA interaction need not equal AD.

Usage

conventional(
  data,
  reference,
  modifier = NULL,
  trial = "study",
  subgroup = "level",
  yi = "yi",
  vi = "vi",
  engine = c("metafor", "bayesmeta"),
  method = NULL,
  approach = NULL,
  tau.prior = NULL,
  mu.prior = NULL,
  ...
)

Arguments

data: A data frame with trial, subgroup, yi, and vi.
reference: Reference subgroup label.
modifier: Column of the two levels. Overrides subgroup when set. The default column remains "subgroup".
trial, subgroup, yi, vi: Column names.
engine: Computational backend. "metafor" (default) or "bayesmeta". CAMS backends are not DA backends. "bayes" is accepted as a synonym of "bayesmeta".
method: Inferential method. NULL (default) is "REML" for metafor and "bayes" for bayesmeta. metafor also accepts "ML". If engine is omitted, "bayes" uses bayesmeta and "REML" / "ML" use metafor.
approach: Inferential approach. "bayes" uses engine = "bayesmeta" when engine is omitted; "frequentist", "REML", or "ML" use engine = "metafor". NULL (default) follows engine / method.
tau.prior, mu.prior: Bayesian priors when engine = "bayesmeta" (defaults: improper uniform on \(\tau\), matching bayesmeta; \(N(0,10)\) on \(\mu\). AD uses the same tau default as CAMS interaction). Ignored by metafor.
...: Computational options passed to metafor::rma() or bayesmeta::bayesmeta() (including engine_args).

Value

An object of class "interactmeta_conventional" containing both DA and AD results from the selected inferential backend.

Details

This is a convenience bundle, not a fourth statistical model. Prefer da() and ad() when you want the constructions separately. swada() applies one common weight vector. cams() fits the CAMS model.

The function is not the joint bivariate mixed model of van Houwelingen, Arends and Stijnen (2002). That paper (and van Houwelingen, Zwinderman and Stijnen 1993) remains the reference for modelling \((y_{Aj}, y_{Bj})\) together with a between-trial heterogeneity matrix \(\Sigma\).

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