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meta-analysis

Synthesize study outcomes with a structured meta-analysis protocol.

Updated Mar 19, 2026
Search Tags:#meta-analysis#heterogeneity#systematic-review#GRADE#effect-size#publication-bias#statistical-models
One-click install
npx skills add https://github.com/sencersoylu/scholar-flow --skill meta-analysis-sencersoylu
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: meta-analysis
Source: https://github.com/sencersoylu/scholar-flow/tree/main/skills/methodology/meta-analysis
Command: npx skills add https://github.com/sencersoylu/scholar-flow --skill meta-analysis-sencersoylu

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Quantitatively synthesizes results across multiple studies by providing a rigorous, step-by-step meta-analysis protocol that guides data extraction, effect-size calculation, model choice, and bias assessment to produce reliable, reproducible conclusions.

Core Features & Use Cases

  • Comprehensive data handling: supports dichotomous, continuous, and time-to-event outcomes with appropriate effect measures (RR, OR, MD, SMD, HR) and data requirements.
  • End-to-end workflow: from eligibility criteria and data extraction to pooling, heterogeneity analysis, subgroup/moderation analyses, and certainty rating using GRADE.
  • Use Case: apply to a series of clinical trials to synthesize treatment effects, assess robustness with sensitivity analyses, and present findings in forest plots and PRISMA-adherent summaries.

Quick Start

Apply the protocol to your included studies by specifying the outcome, data, and study characteristics so the system can compute pooled effects and generate a summary.

Frequently Asked Questions about meta-analysis

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I conduct a meta-analysis for a systematic review with continuous and dichotomous outcomes?▼

Conduct a meta-analysis by applying a structured protocol that handles continuous, dichotomous, and time-to-event outcomes through explicit data extraction, effect-size calculation, and pooling to yield synthesized treatment effects.

What is the best way to assess heterogeneity and publication bias in clinical trial meta-analyses?▼

Assess heterogeneity and publication bias in clinical trial meta-analyses by following predefined steps for pooling, heterogeneity evaluation, and bias assessment, ensuring robust synthesized conclusions.

How do I calculate effect sizes and apply GRADE certainty ratings across multiple study designs?▼

Calculate effect sizes and apply GRADE certainty ratings by adhering to predefined data extraction rules and GRADE standards, evaluating certainty across varied study designs to ensure reliable reporting.

Does this meta-analysis protocol support subgroup and sensitivity analyses for diverse disciplines?▼

Yes, this meta-analysis protocol supports subgroup and sensitivity analyses for diverse disciplines, providing end-to-end workflow guidance from eligibility screening to robustness evaluation.

When should I use fixed versus random statistical models for pooling effect measures?▼

Choose between fixed and random statistical models for pooling effect measures based on the protocol's guidance on model choices and heterogeneity assessment to ensure accurate quantitative synthesis.

What data is required to generate forest plots and PRISMA-adherent summaries for treatment effects?▼

Generating forest plots and PRISMA-adherent summaries requires specifying the outcome, study characteristics, and extracted data so the system can compute pooled effects and present findings.

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