bayesmeta-package {bayesmeta}

Bayesian Random-Effects Meta-Analysis and Meta-Regression

Description

A collection of functions allowing to derive the posterior distribution of the model parameters in random-effects meta-analysis or meta-regression, and providing functionality to evaluate joint and marginal posterior probability distributions, predictive distributions, shrinkage effects, posterior predictive p-values, etc.

Details

Package:	bayesmeta
Type:	Package
Version:	3.4
Date:	2024-02-15
License:	GPL (>=2)

The main functionality is provided by the bayesmeta() and bmr() function. It takes the data (estimates and associated standard errors) and prior information (effect and heterogeneity priors), and returns an object containing functions that allow to derive posterior quantities like joint or marginal densities, quantiles, etc. The bmr() function extends the approach to meta-regression by allowing to specify covariables (moderators) in addition.

Author(s)

Christian Roever <[e-mail]>

References

C. Roever. Bayesian random-effects meta-analysis using the bayesmeta R package. Journal of Statistical Software, 93(6):1-51, 2020. doi:10.18637/jss.v093.i06.

C. Roever, T. Friede. Using the bayesmeta R package for Bayesian random-effects meta-regression. Computer Methods and Programs in Biomedicine, 299:107303, 2023. doi:10.1016/j.cmpb.2022.107303.

See Also

forestplot.bayesmeta, plot.bayesmeta, bmr.

Examples

# example data by Snedecor and Cochran:
data("SnedecorCochran")

## Not run: 
# analysis using improper uniform prior
# (may take a few seconds to compute!):
bma <- bayesmeta(y=SnedecorCochran[,"mean"],
                 sigma=sqrt(SnedecorCochran[,"var"]),
                 label=SnedecorCochran[,"no"])

# show some summary statistics:
bma

# show a bit more details:
summary(bma)

# show a forest plot:
forestplot(bma)

# show some more plots:
plot(bma)

## End(Not run)