ABSTRACT

There are many R packages for meta-analysis and we have so far illustrated

three commonly-used ones: rmeta by Lumley (2009), meta by Schwarzer (2010)

and metafor by Viechtbauer (2010), in the previous chapters. As seen from

the illustration in the previous chapter, all three packages can serve as“general

purpose” packages for arbitrary effect-size and different outcome measures to

fit fixed-effects and random-effects meta-analysis models. From our experience,

all three packages are easy to use for meta-analysis with metafor having more

methods implemented. For example in random-effect meta-analysis, rmeta

only implemented the DerSimonian-Laird estimator to estimate the between-

study variance of τ2 where metafor and meta included several other methods,

such as the restricted maximum-likelihood estimator, the maximum-likelihood

estimator, the Hunter-Schmidt estimator, the Sidik-Jonkman estimator, the

Hedges estimator, and the Empirical Bayes estimator. In addition, metafor

has a great feature for meta-regression as illustrated in Chapter 7 and can

be used to include multiple continuous or categorical regression covariates as

well as mixed-effects models, whereas meta can only include a single categor-

ical regression covariate in fixed-effects, and no meta-regression is included in

rmeta. A comprehensive comparison and discussion of these three packages

can be found in Table 2 of Viechtbauer (2010).