MetaStan-package: The 'MetaStan' package.

In MetaStan: Bayesian Meta-Analysis via 'Stan'

Description

Fitting Bayesian meta-analysis models via Rstan.

Details

To fit meta-analysis models using frequentist methods, there are many R packages available including 'metafor'. On the other hand, Bayesian estimation methods such as Markov chain Monte Carlo (MCMC) are very attractive for meta-analysis, especially because they can be used to fit more complicated models. These include binomial-normal hierarchical models and beta-binomial models which are based on the exact distributional assumptions unlike (commonly used) normal-normal hierarchical model. Another advantage of Bayesian methods to be able to use informative prior distributions for example to regularize heterogeneity estimates in case of low number of studies. Thus, we developed 'MetaStan' which uses Stan (a modern MCMC engine) to fit several pairwise meta-analysis models including binomial-normal hierarchical model and beta-binomial model. This package is also the accompanying package of Guenhan et al (2020). Another important functionality of the package is the model-based meta-analysis models.

Author(s)

Burak Kuersad Guenhan <[e-mail]>

References

Stan Development Team (2018). RStan: the R interface to Stan. R package version 2.17.3. http://mc-stan.org

Günhan, B and Röver, C and Friede, T (2020). Random-effects meta-analysis of few studies involving rare events. Research Synthesis Methods. doi = 10.1002/jrsm.1370.

MetaStan documentation built on Jan. 22, 2022, 5:06 p.m.