Article type
Year
Abstract
Background: Available easy-to-use software packages for metaanalysis do not cope with multiple nested or correlated structures in data (e.g. subjects, hospitals, regions, countries) whereas more flexible and fully capable packages such as WinBUGS require detailed coding.
Objectives: We provide a happy-medium solution to this problem by introducing a free R package whose use is relatively easy and whose capability is nearly as good, if not better, as other command-based software packages.
Methods: Meta-analytic capability has been implemented in MCMCglmm, an R-pacakge for running generalized linear mixed-effects models using MCMC algorithms (i.e. Bayesian hierarchical or multilevel models). MCMCglmm can take arbitrary numbers of fixed effects (moderators) and random effects including a vector of measurement (sampling error) variances and, furthermore, it is able to incorporate any number of correlation matrices.
Results: MCMCglmm has been used in a number of meta-analytic studies, especially in the fields of ecology and evolution, where data are highly complex and heterogeneous due to the inclusion of multiple populations and species. As an illustration of this fairly easy-to-use R package, we present our ‘comparative meta-analysis’ on the effects of dietary restriction on longevity across 36 species and over 100 studies.
Conclusions: MCMCglmm can appropriately model complex meta-analytic data in a relatively easy manner and has potential to be used in meta-analysis in medical and social sciences.
Objectives: We provide a happy-medium solution to this problem by introducing a free R package whose use is relatively easy and whose capability is nearly as good, if not better, as other command-based software packages.
Methods: Meta-analytic capability has been implemented in MCMCglmm, an R-pacakge for running generalized linear mixed-effects models using MCMC algorithms (i.e. Bayesian hierarchical or multilevel models). MCMCglmm can take arbitrary numbers of fixed effects (moderators) and random effects including a vector of measurement (sampling error) variances and, furthermore, it is able to incorporate any number of correlation matrices.
Results: MCMCglmm has been used in a number of meta-analytic studies, especially in the fields of ecology and evolution, where data are highly complex and heterogeneous due to the inclusion of multiple populations and species. As an illustration of this fairly easy-to-use R package, we present our ‘comparative meta-analysis’ on the effects of dietary restriction on longevity across 36 species and over 100 studies.
Conclusions: MCMCglmm can appropriately model complex meta-analytic data in a relatively easy manner and has potential to be used in meta-analysis in medical and social sciences.