BMA.glmm: Bayesian Model-Averaged Generalized Meta-Analysis
In RoBMA: Robust Bayesian Meta-Analyses

BMA.glmm

R Documentation

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,
  ...
)

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 \insertCitejackson2018comparison;textualRoBMA.

Model for incidence rate ratios (measure = "IRR") uses a Poisson-normal model as described in \insertCitebagos2009mixed;textualRoBMA.

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

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.

Examples

## Not run: 
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)
}

## End(Not run)

RoBMA documentation built on May 7, 2026, 5:08 p.m.