Quantifying Systematic Heterogeneity in Meta-Analysis

Quantifying systematic heterogeneity in meta-analysis using R. The M statistic aggregates heterogeneity information across multiple variants to, identify systematic heterogeneity patterns and their direction of effect in meta-analysis. It's primary use is to identify outlier studies, which either show "null" effects or consistently show stronger or weaker genetic effects than average across, the panel of variants examined in a GWAS meta-analysis. In contrast to conventional heterogeneity metrics (Q-statistic, I-squared and tau-squared) which measure random heterogeneity at individual variants, M measures systematic (non-random) heterogeneity across multiple independently associated variants. Systematic heterogeneity can arise in a meta-analysis due to differences in the study characteristics of participating studies. Some of the differences may include: ancestry, allele frequencies, phenotype definition, age-of-disease onset, family-history, gender, linkage disequilibrium and quality control thresholds. See < https://magosil86.github.io/getmstatistic/> for statistical statistical theory, documentation and examples.


Reference manual

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0.2.0 by Lerato E Magosi, a year ago


Report a bug at https://github.com/magosil86/getmstatistic/issues

Browse source code at https://github.com/cran/getmstatistic

Authors: Lerato E Magosi [aut] , Jemma C Hopewell [aut] , Martin Farrall [aut] , Lerato E Magosi [cre]

Documentation:   PDF Manual  

Task views: Meta-Analysis

MIT + file LICENSE license

Imports ggplot2, gridExtra, gtable, metafor, psych, stargazer

Suggests foreign, knitr, testthat

See at CRAN