BMA.glmm: Bayesian Model-Averaged Generalized Meta-Analysis

Description

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,
  ...
)

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.

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.

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 jackson2018comparison;textualRoBMA.

Model for incidence rate ratios (measure = "IRR") uses a Poisson-normal model as described in bagos2009mixed;textualRoBMA.

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

Examples

Run this code

if (FALSE) {
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)
}
}