meta_random: Bayesian Random-Effects Meta-Analysis

In metaBMA: Bayesian Model Averaging for Random and Fixed Effects Meta-Analysis

Description

Bayesian meta-analysis assuming that the effect size d varies across studies with standard deviation τ (i.e., a random-effects model).

Usage

meta_random(
  y,
  SE,
  labels,
  data,
  d = prior("cauchy", c(location = 0, scale = 0.707)),
  tau = prior("invgamma", c(shape = 1, scale = 0.15)),
  rscale_contin = 0.5,
  rscale_discrete = 0.707,
  centering = TRUE,
  logml = "integrate",
  summarize = "stan",
  ci = 0.95,
  rel.tol = .Machine$double.eps^0.3,
  logml_iter = 5000,
  silent_stan = TRUE,
  ...
)

Arguments

y	effect size per study. Can be provided as (1) a numeric vector, (2) the quoted or unquoted name of the variable in data, or (3) a formula to include discrete or continuous moderator variables.
SE	standard error of effect size for each study. Can be a numeric vector or the quoted or unquoted name of the variable in data
labels	optional: character values with study labels. Can be a character vector or the quoted or unquoted name of the variable in data
data	data frame containing the variables for effect size y, standard error SE, labels, and moderators per study.
d	prior distribution on the average effect size d. The prior probability density function is defined via prior.
tau	prior distribution on the between-study heterogeneity tau (i.e., the standard deviation of the study effect sizes dstudy in a random-effects meta-analysis. A (nonnegative) prior probability density function is defined via prior.
rscale_contin	scale parameter of the JZS prior for the continuous covariates.
rscale_discrete	scale parameter of the JZS prior for discrete moderators.
centering	whether continuous moderators are centered.
logml	how to estimate the log-marginal likelihood: either by numerical integration ("integrate") or by bridge sampling using MCMC/Stan samples ("stan"). To obtain high precision with logml="stan", many MCMC samples are required (e.g., logml_iter=10000, warmup=1000).
summarize	how to estimate parameter summaries (mean, median, SD, etc.): Either by numerical integration (summarize = "integrate") or based on MCMC/Stan samples (summarize = "stan").
ci	probability for the credibility/highest-density intervals.
rel.tol	relative tolerance used for numerical integration using integrate. Use rel.tol=.Machine$double.eps for maximal precision (however, this might be slow).
logml_iter	number of iterations (per chain) from the posterior distribution of d and tau. The samples are used for computing the marginal likelihood of the random-effects model with bridge sampling (if logml="stan") and for obtaining parameter estimates (if summarize="stan"). Note that the argument iter=2000 controls the number of iterations for estimation of the random-effect parameters per study in random-effects meta-analysis.
silent_stan	whether to suppress the Stan progress bar.
...	further arguments passed to rstan::sampling (see stanmodel-method-sampling). Relevant MCMC settings concern the number of warmup samples that are discarded (warmup=500), the total number of iterations per chain (iter=2000), the number of MCMC chains (chains=4), whether multiple cores should be used (cores=4), and control arguments that make the sampling in Stan more robust, for instance: control=list(adapt_delta=.97).

Examples

### Bayesian Random-Effects Meta-Analysis (H1: d>0)
data(towels)
set.seed(123)
mr <- meta_random(logOR, SE, study,
  data = towels,
  d = prior("norm", c(mean = 0, sd = .3), lower = 0),
  tau = prior("invgamma", c(shape = 1, scale = 0.15))
)
mr
plot_posterior(mr)

metaBMA documentation built on March 17, 2021, 9:06 a.m.