Robust Bayesian Meta-Analysis (RoBMA) Method

Implements the robust Bayesian meta-analysis (RoBMA) method that uses Bayesian model-averaging to combine results across several complementary publication bias adjustment methods. See Maier et al. (2023) and Bartoš et al. (2023) for details. If "study_id" column is included in the data input, the method uses multilevel parameterization as described in Bartoš et al. (2025) .

Note that the prior settings is dispatched based on "es_type" column attached to the dataset. The resulting estimates are then summarized on the same scale as was the dataset input (for "r", heterogeneity is summarized on Fisher's z).

Important: This method requires JAGS (Just Another Gibbs Sampler) to be installed on your system. Please download and install JAGS from https://mcmc-jags.sourceforge.io/ before using this method.

Usage

# S3 method for class 'RoBMA'
method(method_name, data, settings)

Arguments

method_name

Method name (automatically passed)

data

Data frame with yi (effect sizes), sei (standard errors), es_type (either "SMD" for Cohen's d / Hedge's g, "logOR" for log odds ratio, "z" for Fisher's z, or "r" for correlations. Defaults to "none" which re-scales the default priors to unit-information width based on total sample size supplied "ni".)

settings

List of method settings (see Details.)

Value

Data frame with RoBMA results

Details

The following settings are implemented

"default"

RoBMA-PSMA with publication bias adjustment as described in Bartoš et al. (2023) . (the MCMC settings was reduced to speed-up the simulations) with the three-level specification whenever "study_ids" are supplied with the data

"PSMA"

RoBMA-PSMA with publication bias adjustment as described in Bartoš et al. (2023) . (the MCMC settings was reduced to speed-up the simulations) with the three-level specification whenever "study_ids" are supplied with the data

References

Bartoš F, Maier M, Wagenmakers E (2025). “Robust Bayesian multilevel meta-analysis: Adjusting for publication bias in the presence of dependent effect sizes.” ArXiV Preprint. doi:10.31234/osf.io/9tgp2_v1 .

Bartoš F, Maier M, Wagenmakers E, Doucouliagos H, Stanley TD (2023). “Robust Bayesian meta-analysis: Model-averaging across complementary publication bias adjustment methods.” Research Synthesis Methods, 14(1), 99–116. doi:10.1002/jrsm.1594 .

Maier M, Bartoš F, Wagenmakers E (2023). “Robust Bayesian meta-analysis: Addressing publication bias with model-averaging.” Psychological Methods, 28(1), 107-122. doi:10.1037/met0000405 .

Author

František Bartoš f.bartos96@gmail.com

Examples

# \donttest{
# Generate some example data
data <- data.frame(
  yi      = c(0.2, 0.3, 0.1, 0.4, 0.25),
  sei     = c(0.1, 0.15, 0.08, 0.12, 0.09),
  es_type = "SMD"
)

# Apply RoBMA method (requires RoBMA 3.6.1 version of the package)
#result <- run_method("RoBMA", data)
#print(result)
# }