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

BiBMA {RoBMA}

R Documentation

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 (Bartoš et al. 2023).
`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 (Bartoš et al. 2023).
`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