BiBMA.reg: Estimate a Robust Bayesian Meta-Analysis Meta-Regression

In RoBMA: Robust Bayesian Meta-Analyses

Estimate a Robust Bayesian Meta-Analysis Meta-Regression

Description

RoBMA is used to estimate a robust Bayesian meta-regression. The interface allows a complete customization of the ensemble with different prior (or list of prior) distributions for each component.

Usage

BiBMA.reg(
  formula,
  data,
  test_predictors = TRUE,
  study_names = NULL,
  study_ids = NULL,
  standardize_predictors = TRUE,
  priors = NULL,
  rescale_priors = 1,
  priors_effect = set_default_binomial_priors("effect", rescale = rescale_priors),
  priors_heterogeneity = set_default_binomial_priors("heterogeneity", rescale =
    rescale_priors),
  priors_effect_null = set_default_binomial_priors("effect", null = TRUE),
  priors_heterogeneity_null = set_default_binomial_priors("heterogeneity", null = TRUE),
  prior_covariates = set_default_binomial_priors("covariates", rescale = rescale_priors),
  prior_covariates_null = set_default_binomial_priors("covariates", null = TRUE),
  prior_factors = set_default_binomial_priors("factors", rescale = rescale_priors),
  prior_factors_null = set_default_binomial_priors("factors", null = TRUE),
  priors_baseline = set_default_binomial_priors("baseline"),
  priors_baseline_null = set_default_binomial_priors("baseline", null = TRUE),
  algorithm = "bridge",
  chains = 3,
  sample = 5000,
  burnin = 2000,
  adapt = 500,
  thin = 1,
  parallel = FALSE,
  autofit = TRUE,
  autofit_control = set_autofit_control(),
  convergence_checks = set_convergence_checks(),
  save = "all",
  seed = NULL,
  silent = TRUE,
  ...
)

Arguments

formula	a formula for the meta-regression model
data	a data.frame containing the data for the meta-regression. Note that the column names have to correspond to the effect sizes (d, logOR, OR, r, z), a measure of sampling variability (se, v, n, lCI, uCI, t), and the predictors. See combine_data() for a complete list of reserved names and additional information about specifying input data.
test_predictors	vector of predictor names to test for the presence of moderation (i.e., assigned both the null and alternative prior distributions). Defaults to TRUE, all predictors are tested using the default prior distributions (i.e., prior_covariates, prior_covariates_null, prior_factors, and prior_factors_null). To only estimate and adjust for the effect of predictors use FALSE. If priors is specified, any settings in test_predictors is overridden.
study_names	an optional argument with the names of the studies
study_ids	an optional argument specifying dependency between the studies (for using a multilevel model). Defaults to NULL for studies being independent.
standardize_predictors	whether continuous predictors should be standardized prior to estimating the model. Defaults to TRUE. Continuous predictor standardization is important for applying the default prior distributions for continuous predictors. Note that the resulting output corresponds to standardized meta-regression coefficients.
priors	named list of prior distributions for each predictor (with names corresponding to the predictors). It allows users to specify both the null and alternative hypothesis prior distributions for each predictor by assigning the corresponding element of the named list with another named list (with "null" and "alt"). If only one prior is specified for a given parameter, it is assumed to correspond to the alternative hypotheses and the default null hypothesis is specified (i.e., prior_covariates_null or prior_factors_null). If a named list with only one named prior distribution is provided (either "null" or "alt"), only this prior distribution is used and no default distribution is filled in. Parameters without specified prior distributions are assumed to be only adjusted for using the default alternative hypothesis prior distributions (i.e., prior_covariates or prior_factors). If priors is specified, test_predictors is ignored.
rescale_priors	a re-scaling factor for the prior distributions. The re-scaling factor allows to adjust the width of all default priors simultaneously. Defaults to 1.
priors_effect	list of prior distributions for the effect size (mu) parameter that will be treated as belonging to the alternative hypothesis. Defaults to prior(distribution = "student", parameters = list(location = 0, scale = 0.58, df = 4)), based on logOR meta-analytic estimates from the Cochrane Database of Systematic Reviews \insertCitebartos2023empiricalRoBMA.
priors_heterogeneity	list of prior distributions for the heterogeneity tau parameter that will be treated as belonging to the alternative hypothesis. Defaults to prior(distribution = "invgamma", parameters = list(shape = 1.77, scale = 0.55)) that is based on heterogeneities of logOR estimates from the Cochrane Database of Systematic Reviews \insertCitebartos2023empiricalRoBMA.
priors_effect_null	list of prior distributions for the effect size (mu) parameter that will be treated as belonging to the null hypothesis. Defaults to a point null hypotheses at zero, prior(distribution = "point", parameters = list(location = 0)).
priors_heterogeneity_null	list of prior distributions for the heterogeneity tau parameter that will be treated as belonging to the null hypothesis. Defaults to a point null hypotheses at zero (a fixed effect meta-analytic models), prior(distribution = "point", parameters = list(location = 0)).
prior_covariates	a prior distributions for the regression parameter of continuous covariates on the effect size under the alternative hypothesis (unless set explicitly in priors). Defaults to a relatively wide normal distribution prior(distribution = "normal", parameters = list(mean = 0, sd = 0.25)).
prior_covariates_null	a prior distributions for the regression parameter of continuous covariates on the effect size under the null hypothesis (unless set explicitly in priors). Defaults to a no effect prior("spike", parameters = list(location = 0)).
prior_factors	a prior distributions for the regression parameter of categorical covariates on the effect size under the alternative hypothesis (unless set explicitly in priors). Defaults to a relatively wide multivariate normal distribution specifying differences from the mean contrasts prior_factor("mnormal", parameters = list(mean = 0, sd = 0.25), contrast = "meandif").
prior_factors_null	a prior distributions for the regression parameter of categorical covariates on the effect size under the null hypothesis (unless set explicitly in priors). Defaults to a no effect prior("spike", parameters = list(location = 0)).
priors_baseline	prior distributions for the alternative hypothesis about intercepts (pi) of each study. Defaults to NULL.
priors_baseline_null	prior distributions for the null hypothesis about intercepts (pi) for each study. Defaults to an independent uniform prior distribution for each intercept prior("beta", parameters = list(alpha = 1, beta = 1), contrast = "independent").
algorithm	a string specifying the algorithm used for the model averaging. Defaults to "bridge" which results in estimating individual models using JAGS and computing the marginal likelihood using bridge sampling. An alternative is "ss" which uses spike and slab like parameterization to approximate the Bayesian model averaging with a single model. Note that significantly more sample, burnin, and adapt iterations are needed for the "ss" algorithm.
chains	a number of chains of the MCMC algorithm.
sample	a number of sampling iterations of the MCMC algorithm. Defaults to 5000.
burnin	a number of burnin iterations of the MCMC algorithm. Defaults to 2000.
adapt	a number of adaptation iterations of the MCMC algorithm. Defaults to 500.
thin	a thinning of the chains of the MCMC algorithm. Defaults to 1.
parallel	whether the individual models should be fitted in parallel. Defaults to FALSE. The implementation is not completely stable and might cause a connection error.
autofit	whether the model should be fitted until the convergence criteria (specified in autofit_control) are satisfied. Defaults to TRUE.
autofit_control	allows to pass autofit control settings with the set_autofit_control() function. See ?set_autofit_control for options and default settings.
convergence_checks	automatic convergence checks to assess the fitted models, passed with set_convergence_checks() function. See ?set_convergence_checks for options and default settings.
save	whether all models posterior distributions should be kept after obtaining a model-averaged result. Defaults to "all" which does not remove anything. Set to "min" to significantly reduce the size of final object, however, some model diagnostics and further manipulation with the object will not be possible.
seed	a seed to be set before model fitting, marginal likelihood computation, and posterior mixing for reproducibility of results. Defaults to NULL - no seed is set.
silent	whether all print messages regarding the fitting process should be suppressed. Defaults to TRUE. Note that parallel = TRUE also suppresses all messages.
...	additional arguments.

Details

BiBMA.reg() function estimates the Bayesian model-averaged binomial meta-regression. See vignette("/MetaRegression", package = "RoBMA") vignette describes how to use the similar RoBMA.reg() function to fit Bayesian meta-regression ensembles. See \insertCitebartos2023robust;textualRoBMA for more details about the methodology and BiBMA() for more details about the function options. By default, the function standardizes continuous predictors. As such, the output should be interpreted as standardized meta-regression coefficients.

Generic summary.RoBMA(), print.RoBMA(), and plot.RoBMA() functions are provided to facilitate manipulation with the ensemble. A visual check of the individual model diagnostics can be obtained using the diagnostics() function. The fitted model can be further updated or modified by update.RoBMA() function. Estimated marginal means can be computed by marginal_summary() function and visualized by the marginal_plot() function.

Value

RoBMA.reg returns an object of class 'RoBMA.reg'.

References

\insertAllCited

See Also

BiBMA() summary.RoBMA(), update.BiBMA(), check_setup.reg()

RoBMA documentation built on April 12, 2025, 1:55 a.m.