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

BiBMA estimate a binomial-normal 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 bartos2023empiricalRoBMA.
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 bartos2023empiricalRoBMA.
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.

Value

NoBMA returns an object of class 'RoBMA'.

Details

The BiBMA() function estimates the binomial-normal Bayesian model-averaged meta-analysis described in bartos2023empirical;textualRoBMA. See vignette("MedicineBiBMA", package = "RoBMA") vignette for a reproduction of the oduwole2018honey;textualRoBMA example. Also RoBMA() for additional details.

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.

References

Examples

if (FALSE) { # \dontrun{
# using the example data from Oduwole (2018) and reproducing the example from
# Bartos et al. (2023) with domain specific informed prior distributions

fit <- BiBMA(
  x1          = c(5, 2),
  x2          = c(0, 0),
  n1          = c(35, 40),
  n2          = c(39, 40),
  priors_effect        = prior_informed(
      "Acute Respiratory Infections",
      type = "logOR", parameter = "effect"),
  priors_heterogeneity = prior_informed(
      "Acute Respiratory Infections",
      type = "logOR", parameter = "heterogeneity")
 )

 summary(fit)

 # produce summary on OR scale
 summary(fit, output_scale = "OR")

} # }