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.)
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
.
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
result <- run_method("RoBMA", data)
#> Loading required namespace: runjags
#> Loading required namespace: mvtnorm
print(result)
#> method estimate standard_error ci_lower ci_upper p_value BF
#> 1 RoBMA 0.07245329 NA 0 0.2827614 NA 0.7532874
#> convergence note tau_estimate tau_ci_lower tau_ci_upper tau_BF
#> 1 TRUE TRUE 0.04087817 0 0.252459 0.4609204
#> bias_SM_coefficient
#> 1 1, 0.91543706489259, 0.831569767534035, 0.804442561500065, 0.814358728230785, 0.847369579189188
#> bias_SM_coefficient_ci_lower
#> 1 1, 0.189202106868124, 0.0413680937549003, 0.0211984484761842, 0.0217220354856666, 0.0223131588475772
#> bias_SM_coefficient_ci_upper bias_PP_coefficient
#> 1 1, 1, 1, 1, 1, 1 0.695002655675012, 3.60584982099692
#> bias_PP_coefficient_ci_lower bias_PP_coefficient_ci_upper bias_BF
#> 1 0, 0 2.9168083740665, 22.8263307590521 5.263982
#> method_setting
#> 1 default
# }