getmstatistic (version 0.2.2)
Quantifying Systematic Heterogeneity in Meta-Analysis
Description
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.