getmstatistic (0.2.0)

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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 for statistical statistical theory, documentation and examples.

Maintainer: Lerato E Magosi
Author(s): Lerato E Magosi [aut], Jemma C Hopewell [aut], Martin Farrall [aut], Lerato E Magosi [cre]

License: MIT + file LICENSE

Uses: ggplot2, gridExtra, gtable, metafor, psych, stargazer, foreign, testthat, knitr

Released 10 months ago.

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