{"enrichment":{"faq":[{"a":"tooluniverse-meta-analysis combines quantitative results from two or more studies into a pooled estimate and confidence interval, with built-in heterogeneity assessment (I\u00b2, Q, \u03c4\u00b2). It converts raw reported values\u2014odds ratios, risk ratios, hazard ratios, means, proportions, correlations\u2014into standardized (effect, standard error) format, then applies fixed- or random-effects models and generates a forest plot to visualize agreement across studies.","q":"What does tooluniverse-meta-analysis do?"},{"a":"Yes. tooluniverse-meta-analysis is designed to pool effect sizes from multiple studies into a single estimate with confidence interval. It handles conversion of diverse study result formats\u2014OR, RR, HR, means, proportions, and correlations\u2014into the standardized effect and standard error format required for pooling, then synthesizes them using either fixed- or random-effects models depending on heterogeneity.","q":"Can tooluniverse-meta-analysis pool effect sizes from multiple studies?"},{"a":"tooluniverse-meta-analysis assesses heterogeneity using I\u00b2, Q statistic, and \u03c4\u00b2 (tau-squared), helping you decide between fixed- and random-effects models. These metrics quantify the proportion of variation due to true study differences rather than sampling error, guiding model selection for accurate pooled estimates.","q":"How does tooluniverse-meta-analysis assess heterogeneity?"},{"a":"tooluniverse-meta-analysis converts odds ratios, risk ratios, hazard ratios, means, proportions, and correlations (including Hedges g and mean differences) to standardized effect and standard error format. It handles log transformation of ratio measures and confidence interval-to-SE conversion, preparing all study results for pooling.","q":"What formats can tooluniverse-meta-analysis convert to effect+SE?"},{"a":"Yes. tooluniverse-meta-analysis generates forest plots to visualize pooled estimates and individual study results, making it easy to see agreement or disagreement across studies. The plot displays heterogeneity visually alongside the synthesized systematic review evidence and overall pooled effect.","q":"Does tooluniverse-meta-analysis generate forest plots?"},{"a":"tooluniverse-meta-analysis can synthesize multi-cohort association results by pooling effect sizes and standard errors across replicated experiments or cohorts. It applies random- or fixed-effects models to aggregate study results into a single estimate, making it applicable to large-scale evidence synthesis including GWAS meta-analysis.","q":"Is tooluniverse-meta-analysis suitable for multi-cohort GWAS?"}],"shadow_tags":["effect-size-pooling","heterogeneity-assessment","systematic-review-stats","evidence-aggregation","ratio-measure-conversion","forest-plot-visualization","between-study-variance","ci-to-se-transformation"],"summary_rewrite":"Combine quantitative results from two or more studies into a pooled estimate and confidence interval, with built-in heterogeneity assessment (I\u00b2, Q, \u03c4\u00b2). The skill converts raw reported values\u2014odds ratios, risk ratios, hazard ratios, means, proportions, correlations\u2014into the standardized (effect, standard error) format pooling requires, then applies fixed- or random-effects models and generates a forest plot to visualize agreement across studies."},"files":[{"bytes":8028,"path":"skills/tooluniverse-meta-analysis/SKILL.md","sha256":"6f8c042fed5d5f39985783e0020cef6aed109cf9861fa2daa048bab7ec5c6c97","url":"https://skillfed.io/files/mims-harvard/ToolUniverse/tooluniverse-meta-analysis/11a44180/SKILL.md"}],"id":"mims-harvard/ToolUniverse/tooluniverse-meta-analysis","links":{"html":"https://skillfed.io/mims-harvard/ToolUniverse/tooluniverse-meta-analysis","md":"https://skillfed.io/mims-harvard/ToolUniverse/tooluniverse-meta-analysis.md","repo":"https://github.com/mims-harvard/ToolUniverse"},"meta":{"agents_supported":[],"first_seen":"2026-07-28","forks":242,"language":"Python","last_updated":"2026-07-27","license":"Apache-2.0","name":"tooluniverse-meta-analysis","publisher":"mims-harvard","stars":1595},"relations":{"similar":[{"id":"beita6969/ScienceClaw/meta-analysis"},{"id":"beita6969/ScienceClaw/systematic-review"},{"id":"mims-harvard/ToolUniverse/tooluniverse-gwas-study-explorer"},{"id":"beita6969/ScienceClaw/statistical-testing"},{"id":"jaechang-hits/SciAgent-Skills/literature-review"},{"id":"jaechang-hits/SciAgent-Skills/scientific-critical-thinking"},{"id":"lyndonkl/claude/domain-research-health-science"},{"id":"drshailesh88/integrated_content_OS/statistical-analysis"},{"id":"mims-harvard/ToolUniverse/tooluniverse"},{"id":"Aradotso/marketing-skills/marketing-science-writing"}]},"slug":{"owner":"mims-harvard","repo":"ToolUniverse","skill":"tooluniverse-meta-analysis"},"version":"11a44180"}