BiBMA: Estimate a Bayesian Model-Averaged Meta-Analysis of Binomial...

In RoBMA: Robust Bayesian Meta-Analyses

Estimate a Bayesian Model-Averaged Meta-Analysis of Binomial Data

Description

BiBMA estimate a Binomial Bayesian model-averaged meta-analysis. The interface allows a complete customization of the ensemble with different prior (or list of prior) distributions for each component.

Usage

BiBMA(
  x1,
  x2,
  n1,
  n2,
  study_names = NULL,
  study_ids = NULL,
  priors_effect = prior(distribution = "student", parameters = list(location = 0, scale =
    0.58, df = 4)),
  priors_heterogeneity = prior(distribution = "invgamma", parameters = list(shape = 1.77,
    scale = 0.55)),
  priors_effect_null = prior(distribution = "point", parameters = list(location = 0)),
  priors_heterogeneity_null = prior(distribution = "point", parameters = list(location =
    0)),
  priors_baseline = NULL,
  priors_baseline_null = prior_factor("beta", parameters = list(alpha = 1, beta = 1),
    contrast = "independent"),
  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

x1	a vector with the number of successes in the first group
x2	a vector with the number of successes in the second group
n1	a vector with the number of observations in the first group
n2	a vector with the number of observations in the second group
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.
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)).
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").
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

See RoBMA() for more details.

Value

NoBMA returns an object of class 'RoBMA'.

See Also

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

RoBMA documentation built on July 26, 2023, 5:13 p.m.