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  "Title": "Quantifying Systematic Heterogeneity in Meta-Analysis",
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  "Authors@R": "c(\nperson(\"Lerato E\", \"Magosi\", email = \"lmagosi@well.ox.ac.uk\",\nrole = \"aut\",),\nperson(\"Jemma C\", \"Hopewell\", email = \"jemma.hopewell@ndph.ox.ac.uk\",\nrole = \"aut\"),\nperson(\"Martin\", \"Farrall\", email = \"mfarrall@well.ox.ac.uk\",\nrole = \"aut\"),\nperson(\"Lerato E\", \"Magosi\", email = \"magosil86@gmail.com\",\nrole = \"cre\")\n)",
  "Description": "Quantifying systematic heterogeneity in meta-analysis\nusing R. The M statistic aggregates heterogeneity information\nacross multiple variants to, identify systematic heterogeneity\npatterns and their direction of effect in meta-analysis. It's\nprimary use is to identify outlier studies, which either show\n\"null\" effects or consistently show stronger or weaker genetic\neffects than average across, the panel of variants examined in\na GWAS meta-analysis. In contrast to conventional heterogeneity\nmetrics (Q-statistic, I-squared and tau-squared) which measure\nrandom heterogeneity at individual variants, M measures\nsystematic (non-random) heterogeneity across multiple\nindependently associated variants. Systematic heterogeneity can\narise in a meta-analysis due to differences in the study\ncharacteristics of participating studies. Some of the\ndifferences may include: ancestry, allele frequencies,\nphenotype definition, age-of-disease onset, family-history,\ngender, linkage disequilibrium and quality control thresholds.\nSee <https://magosil86.github.io/getmstatistic/> for\nstatistical statistical theory, documentation and examples.",
  "License": "MIT + file LICENSE",
  "URL": "https://magosil86.github.io/getmstatistic/",
  "BugReports": "https://github.com/magosil86/getmstatistic/issues",
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  "Date/Publication": "2021-05-08 22:10:29 UTC",
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  "Author": "Lerato E Magosi [aut],\nJemma C Hopewell [aut],\nMartin Farrall [aut],\nLerato E Magosi [cre]",
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    "gwas",
    "heartgenes214",
    "heterogeneity",
    "meta-analysis",
    "mstatistic",
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    "stata",
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      "title": "Quantifying Systematic Heterogeneity in Meta-Analysis.",
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      "filename": "getmstatistic-tutorial.html",
      "title": "The M statistic: A simple method to measure the impact of systematic heterogeneity in GWAS meta-analysis.",
      "author": "Lerato E. Magosi",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Background",
        "Properties of the M-statistic",
        "See the M statistic paper for details on the statistical theory:",
        "This vignette walks through the steps involved in carrying out an M analysis",
        "A few tips for a succesful M analysis",
        "Data: heartgenes214",
        "We shall use the heartgenes214 dataset for our M analysis.",
        "Note:",
        "The data was sourced from:",
        "The basics: Conducting an M analysis",
        "Understanding the plots generated by getmstatistic",
        "A step further: Exploring available options",
        "Generating M statistic forest plots"
      ],
      "created": "2017-04-25 01:41:36",
      "modified": "2021-03-14 18:00:57",
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