CRAN Task View: Meta-Analysis

Maintainer:Michael Dewey
Contact:info at aghmed.fsnet.co.uk
Version:2014-07-03

This task view covers packages which include facilities for meta-analysis of summary statistics from primary studies. The task view does not consider the meta-analysis of individual participant data which can be handled by any of the standard linear modelling functions.

The standard meta-analysis model is a form of weighted least squares and so any of the wide range of R packages providing weighted least squares would in principle be able to fit the model. The advantage of using a specialised package is that (a) it takes care of the small tweaks necessary (b) it provides a range of ancillary functions for displaying and investigating the model. Where the model is referred to below it is this model which is meant.

Where summary statistics are not available a meta-analysis of significance levels is possible. This is not completely unconnected with the problem of adjustment for multiple comparisons but the packages below which offer this, chiefly in the context of genetic data, also offer additional functionality.

Univariate meta-analysis

Preparing for meta-analysis

Fitting the model

Graphical methods

An extensive range of graphical procedures is available:

Investigating heterogeneity

Confidence intervals for the heterogeneity parameter are provided in metafor and metagen.

Investigating small study bias

The issue of whether small studies give different results from large studies has been addressed by visual examination of the funnel plots mentioned above. In addition:

Unobserved studies

A recurrent issue in meta-analysis has been the problem of unobserved studies.

Meta-analysis of significance values

metap provides some facilities for meta-analysis of significance values. Some of these methods are also provided in some of the genetics packages mentioned below.

Multivariate meta-analysis

Standard methods outlined above assume that the effect sizes are independent. This assumption may be violated in a number of ways: within each primary study multiple treatments may be compared to the same control, each primary study may report multiple endpoints, or primary studies may be clustered for instance because they come from the same country or the same research team. In these situations where the outcome is multivariate:

Meta-analysis of studies of diagnostic tests

A special case of multivariate meta-analysis is the case of summarising studies of diagnostic tests. This gives rise to a bivariate, binary meta-analysis with the within-study correlation assumed zero although the between-study correlation is estimated. This is an active area of research and a variety of methods are available including what is referred to here called Reitsma's method, and the heirarchical summary receiver operating characteristic (HSROC) method. In many situations these are equivalent.

Meta-regression

Where suitable moderator variables are available they may be included using meta-regression. All these packages are mentioned above, this just draws that information together.

Network meta-analysis

Also known as multiple treatment comparison. This is a very active area of research and development. Note that some of the packages mentioned above under multivariate meta-analysis can also be used for network meta-analysis with appropriate setup.

This is provided in a Bayesian framework by gemtc, which acts as a front-end to your favourite MCMC package, and pcnetmeta, which uses JAGS. netmeta works in a frequentist framework. Both pcnetmeta and netmeta provide network graphs and netmeta provides a heatmap for displaying inconsistency and heterogeneity

Genetics

There are a number of packages specialising in genetic data: gap combines p-values, MADAM combines p-values using Fisher's method, MAMA provides meta-analysis of microarray data, MetABEL provides meta-analysis of genome wide SNP association results, MetaDE provides microarray meta-analysis of for differentially expressed dene detection, metaMA provides meta-analysis of p-values or moderated effect sizes to find differentially expressed genes, MetaPCA provides meta-analysis in the dimension reduction of genomic data, MetaQC provides objective quality control and inclusion/exclusion criteria for genomic meta-analysis, MetaSKAT, seqMeta, and skatMeta provide meta-analysis for the SKAT test.

Others

SCMA provides single case meta-analysis. It is part of a suite of packages dedicated to single-case designs.

CRTSize provides meta-analysis as part of a package primarily dedicated to the determination of sample size in cluster randomised trials.

CAMAN offers the possibility of using finite semiparametric mixtures as an alternative to the random effects model where there is heterogeneity. Covariates can be included to provide meta-regression.

RcmdrPlugin.MA and RcmdrPlugin.EZR provide an interface via the Rcmdr GUI. The former uses metafor and MAd and the latter uses meta and metatest to do the heavy lifting.

CRAN packages:

Related links: