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

Overview

Fixed-effects meta-analyses assume that the effect size $d$ is identical in all studies. In contrast, random-effects meta-analyses assume that effects vary according to a normal distribution with mean $d$ and standard deviation $\tau$. Both models can be compared in a Bayesian framework by assuming specific prior distribution for $d$ and $\tau$. Given the posterior model probabilities, the evidence for or against an effect (i.e., whether $d = 0$) and the evidence for or against random effects can be evaluated (i.e., whether $\tau = 0$). By using Bayesian model averaging (i.e., inclusion Bayes factors), both types of tests can be performed by marginalizing over the other question. Most importantly, this allows to test whether an effect exists while accounting for uncertainty whether study heterogeneity exists or not.

Defining and Plotting Priors

To fit a meta-analysis model, prior distributions on the average effect $d$ and the heterogeneity $\tau$ are required. The package metaBMA leaves the user the freedom to choose from several predefined distributions or even define an owen prior density function. The function prior facilitates the construction and visual inspection of prior distributions to check whether they meet the prior knowledge about the field of interest.

# load package
library("metaBMA")
# load data set
data(towels)

# Half-normal (truncated to > 0)
p1 <- prior("norm", c(mean=0, sd=.3), lower = 0)
p1
p1(1:3)
plot(p1)

# custom prior
p1 <- prior("custom", function(x) x^3-2*x+3, lower = 0, upper = 1)
plot(p1, -.5, 1.5)

Bayesian Meta-Analysis

The functions meta_fixed() and meta_random() fit Bayesian meta-analysis models. The model-specific posteriors for $d$ can then be averaged by bma() and inclusion Bayes factors be computed by inclusion().

The fixed-effects meta-analysis assumes that the effect size is identical across studies. This model requires only one prior distribution for the overall effect $d$:

# Fixed-effects
progres <- capture.output(  # suppress Stan progress for vignette
  mf <- meta_fixed(logOR, SE, study, towels, 
                   d = prior("norm", c(mean=0, sd=.3), lower=0))
)
mf

# plot posterior distribution
plot_posterior(mf)

In contrast, the random-effects meta-analysis assumes that the effect size varies across studies. Specifically, it is assumed that study effect sizes follow a normal distribution with mean $d$ and standard deviation $\tau$. This model requires two prior distributions for both parameters:

# Random-effects
progres <- capture.output(  # suppress Stan progress for vignette
  mr <- meta_random(logOR, SE, study, towels,
                    d = prior("norm", c(mean=0, sd=.3), lower=0),
                    tau = prior("t", c(location=0, scale=.3, nu=1), lower=0),
                    iter = 1500, logml_iter = 2000, rel.tol = .1)
)
mr  

# plot posterior distribution
plot_posterior(mr, main = "Average effect size d")
plot_posterior(mr, "tau", main = "Heterogeneity tau")

Model-Averaging for Meta-Analysis

The most general functions in metaBMA are meta_bma() and meta_default(), which fit random- and fixed-effects models, compute the inclusion Bayes factor for the presence of an effect and the averaged posterior distribution of the mean effect $d$ (which accounts for uncertainty regarding study heterogeneity).

mb <- meta_bma(logOR, SE, study, towels,
               d = prior("norm", c(mean=0, sd=.3), lower=0),
               tau = prior("t", c(location=0, scale=.3, nu=1), lower=0),
                    iter = 1500, logml_iter = 2000, rel.tol = .1)
mb
plot_posterior(mb, "d", -.1, 1.4)
plot_forest(mb)

Predicted Bayes Factor for New Study

Often, it is of interest to judge how much additional evidence future studies can contribute to the present knowledge. Conditional on the outcome of the model averaging for meta-analysis, the function predicted_bf() samples new data sets from the posterior and performs model selection for each replication. Thereby, a distribution of predicted Bayes factors is obtain that represents the expected evidence one expects when running a new study. The following example is not executed since it requires time-intensive computations:

mp <- predicted_bf(mb, SE = .2, sample = 30)
plot(mp)


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