Bayesian Model-Averaged Generalized Meta-Analysis

Function for fitting Bayesian model-averaged meta-analytic models directly to binary or count data using a generalized linear mixed model (GLMM) framework. Unlike RoBMA, this function does not adjust for publication bias, as weight function and regression-based bias adjustment methods are not available for GLMM outcomes.

Usage

BMA.glmm(
  ai,
  bi,
  ci,
  di,
  n1i,
  n2i,
  x1i,
  x2i,
  t1i,
  t2i,
  weights,
  mods,
  scale,
  cluster,
  data,
  slab,
  subset,
  measure = "OR",
  prior_effect,
  prior_heterogeneity,
  prior_mods,
  prior_scale,
  prior_heterogeneity_allocation,
  prior_baserate,
  prior_lograte,
  prior_effect_null,
  prior_heterogeneity_null,
  prior_mods_null,
  prior_scale_null,
  prior_heterogeneity_allocation_null,
  standardize_continuous_predictors = TRUE,
  set_contrast_factor_predictors = "treatment",
  prior_unit_information_sd,
  rescale_priors = 1,
  prior_informed_field,
  prior_informed_subfield,
  sample = 5000,
  burnin = 2000,
  adapt = 500,
  chains = 3,
  thin = 1,
  parallel = FALSE,
  autofit = FALSE,
  autofit_control = set_autofit_control(),
  convergence_checks = set_convergence_checks(),
  seed = NULL,
  silent = TRUE,
  ...
)

Arguments

ai: a vector of the number of events in the treatment or experimental group for binomial GLMM models.
bi: a vector of the number of non-events in the treatment or experimental group for binomial GLMM models.
ci: a vector of the number of events in the control group for binomial GLMM models.
di: a vector of the number of non-events in the control group for binomial GLMM models.
n1i: a vector of the sample size in the treatment or experimental group. If omitted for binomial GLMMs, it is computed as ai + bi.
n2i: a vector of the sample size in the control group. If omitted for binomial GLMMs, it is computed as ci + di.
x1i: a vector of the number of events in the treatment/experimental group (for Poisson data).
x2i: a vector of the number of events in the control group (for Poisson data).
t1i: a vector of the person-time in the treatment/experimental group.
t2i: a vector of the person-time in the control group.
weights: an optional vector of positive likelihood weights. For normal/effect-size models, each weight powers the estimate likelihood. For constructors with GLMM raw-count input, each weight powers the paired two-arm likelihood for one study.
mods: an optional matrix, data.frame, or formula specifying location moderators (meta-regressors). Formula input is evaluated in data.
scale: an optional matrix, data.frame, or formula specifying scale predictors for location-scale models. Formula input is evaluated in data.
cluster: an optional vector of cluster identifiers for multilevel meta-analysis.
data: an optional data frame containing the variables.
slab: an optional vector of study labels.
subset: an optional logical or numeric vector specifying a subset of data to be used.
measure: a character string specifying the effect size measure. Normal/effect-size constructors require an explicit value and support "SMD", "ZCOR", "RR", "OR", "HR", "RD", "IRR", and "GEN". Use "GEN" only for general effect sizes without a known unit information standard deviation. GLMM raw-count constructors support only "OR" and "IRR" and default to "OR".
prior_effect: prior distribution(s) for the alternative effect component(s).
prior_heterogeneity: prior distribution(s) for the alternative heterogeneity component(s).
prior_mods: prior distribution(s) for alternative moderator components. A single prior applies to all terms; a named list can specify term-specific components.
prior_scale: prior distribution(s) for alternative scale-regression components. A single prior applies to all terms; a named list can specify term-specific components.
prior_heterogeneity_allocation: prior distribution(s) for the alternative cluster-level heterogeneity allocation component(s).
prior_baserate: prior distribution for the estimate-specific midpoint base-rate probability in binomial GLMM models. If omitted or NULL, defaults to independent Beta(1, 1) priors.
prior_lograte: prior distribution for the estimate-specific midpoint log-rate in Poisson GLMM models. If omitted or NULL, a data-based unit-information normal prior is used independently for each estimate.
prior_effect_null: prior distribution(s) for the null effect component(s).
prior_heterogeneity_null: prior distribution(s) for the null heterogeneity component(s).
prior_mods_null: prior distribution(s) for null moderator components. A single prior applies to all terms; a named list can specify term-specific components.
prior_scale_null: prior distribution(s) for null scale-regression components. A single prior applies to all terms; a named list can specify term-specific components.
prior_heterogeneity_allocation_null: prior distribution(s) for the null cluster-level heterogeneity allocation component(s).
standardize_continuous_predictors: logical. Whether to standardize continuous predictors. Defaults to TRUE.
set_contrast_factor_predictors: character. How to set contrast for factor predictors. Defaults are constructor-specific and shown in each function usage; single-model constructors use "treatment", while model-averaging constructors use "meandif".
prior_unit_information_sd: numeric. The unit information standard deviation (\(\sigma_{unit}\)). Cannot be used together with prior_informed_field.
rescale_priors: numeric. A scaling factor for supported prior distributions. Point and none priors are unchanged. For constructors with publication-bias prior distributions, rescale_priors does not rescale them except for the default PEESE prior's UISD adjustment. Defaults to 1.
prior_informed_field: character. The field of the informed prior distributions. Omit to use the standard default prior specification; explicit NULL is invalid.
prior_informed_subfield: character. The subfield of the informed prior distributions. Omit to use the field-specific default, such as "Cochrane" for prior_informed_field = "medicine"; explicit NULL is invalid.
sample: numeric. Number of MCMC samples to save. Defaults to 5000.
burnin: numeric. Number of burn-in iterations. Defaults to 2000.
adapt: numeric. Number of adaptation iterations. Defaults to 500.
chains: numeric. Number of MCMC chains. Defaults to 3.
thin: numeric. Thinning interval. Defaults to 1.
parallel: logical. Whether to run MCMC chains in parallel. Defaults to FALSE.
autofit: logical. Whether to automatically extend the MCMC chains if convergence is not met. Defaults to FALSE.
autofit_control: list of autofit control settings. See set_autofit_control() for details.
convergence_checks: list of convergence check settings. See set_convergence_checks() for details.
seed: numeric. Random seed for reproducibility. Defaults to NULL.
silent: logical. Whether to suppress output. Constructors with no explicit default use RoBMA.get_option("silent") when silent is omitted. Model-averaging wrappers default to TRUE unless explicitly changed.
...: additional advanced arguments. Fitting functions reject unused arguments; currently recognized internal arguments include only_data, only_priors, is_JASP, and is_JASP_prefix.

Value

A fitted object of class c("BMA.glmm", "RoBMA", "brma.glmm", "brma"). The object contains checked data, checked mixture priors, the JAGS fit, cached summary, and cached coefficients.

Details

BMA.glmm combines the data input style of brma.glmm with the mixture prior specification of RoBMA for Bayesian model-averaging.

Model for odds ratios (measure = "OR") uses a binomial-normal model as described in Jackson et al. (2018) .

Model for incidence rate ratios (measure = "IRR") uses a Poisson-normal model as described in Bagos and Nikolopoulos (2009) .

When weights are supplied, they are treated as likelihood weights on the paired two-arm study contribution.

Examples

if (FALSE) { # \dontrun{
if (requireNamespace("metadat", quietly = TRUE)) {
  data(dat.bcg, package = "metadat")

  fit <- BMA.glmm(
    ai      = tpos,
    bi      = tneg,
    ci      = cpos,
    di      = cneg,
    data    = dat.bcg,
    measure = "OR",
    seed    = 1,
    silent  = TRUE
  )

  summary(fit)
}
} # }