Quantifying Systematic Heterogeneity in Meta-Analysis
c(
person("Lerato E", "Magosi", email = "lmagosi@well.ox.ac.uk",
role = "aut",),
person("Jemma C", "Hopewell", email = "jemma.hopewell@ndph.ox.ac.uk",
role = "aut"),
person("Martin", "Farrall", email = "mfarrall@well.ox.ac.uk",
role = "aut"),
person("Lerato E", "Magosi", email = "lmagosi@well.ox.ac.uk",
role = "cre")
)
Quantifying systematic heterogeneity in meta-analysis using R.
The \code{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, \code{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.
Lerato E Magosi [aut],
Jemma C Hopewell [aut],
Martin Farrall [aut],
Lerato E Magosi [cre]

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.