Abstract
Meta-analysis is a statistical method for combining information from different studies about the same issue of interest. Meta-analysis is widely diffuse in medical investigation and more recently it received a growing interest also in social disciplines. Typical applications involve a small number of studies, thus making ordinary inferential methods based on first-order asymptotics unreliable. More accurate results can be obtained by exploiting the theory of higher-order asymptotics. This paper describes the <b>metaLik</b> package which provides an R implementation of higher-order likelihood methods in meta-analysis. The extension to meta-regression is included. Two real data examples are used to illustrate the capabilities of the package.
Highlights
Meta-analysis is a statistical method for pooling the results from multiple separate studies about the same issue of interest
The package metaLik described in this paper extends the likelihood approach to meta-analysis and meta-regression to guarantee higher accuracy of the asymptotic results, which is better appreciable in case of small sample sizes
Higher-order asymptotics literature has had an important impact on methodological journals in the last years, as the detailed review by Reid (2003) pointed out
Summary
Meta-analysis is a statistical method for pooling the results from multiple separate studies about the same issue of interest. The approach is prone to substantial disadvantages, giving rise to unreliable inferential results These can be experienced mainly in case of small sample size. Standard first-order likelihood approximations can metaLik: Likelihood Inference in Meta-Analysis in R be inaccurate because of the typical small sample sizes of meta-analysis. To overcome such difficulties, Guolo (2012) shows that accurate inferential conclusions can be restored by exploiting the theory of higher-order asymptotics. The package metaLik described in this paper extends the likelihood approach to meta-analysis and meta-regression to guarantee higher accuracy of the asymptotic results, which is better appreciable in case of small sample sizes.
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