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.

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, 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 .

Examples

if (FALSE) { # \dontrun{
# 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
result <- run_method("RoBMA", data)
print(result)
} # }