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data-extraction

Convert study data into effect sizes and variances for R metafor pipelines.

1|Updated Dec 29, 2025
Search Tags:#meta-analysis#data-extraction#effect-size#data-preparation#metafor#missing-data#escalc
One-click install
npx skills add https://github.com/matheus-rech/meta-agent-mobile --skill data-extraction-matheus-rech
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: data-extraction
Source: https://github.com/matheus-rech/meta-agent-mobile/tree/main/agentskills/data-extraction
Command: npx skills add https://github.com/matheus-rech/meta-agent-mobile --skill data-extraction-matheus-rech

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

The skill helps researchers convert study data into analyzable effect sizes and variances for meta-analysis, reducing manual calculation errors and time.

Core Features & Use Cases

  • Data extraction and conversion from binary and continuous outcomes to common metrics (OR, RR, SMD, MD).
  • Automated variance estimation and handling of missing data for incomplete reports.
  • Use Case: Given a set of studies with mixed reporting formats, convert to standardized effect sizes and prepare a ready-to-analyze dataset for R metafor pipelines.

Quick Start

Input a study dataset and run the data-extraction workflow to generate standardized effect sizes and variances.

Frequently Asked Questions about data-extraction

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

FAQPage Schema
How do I convert study data with mixed reporting formats into standardized effect sizes for meta-analysis?▼

To convert study data for meta-analysis, the Skill extracts binary and continuous outcomes, converting them to common metrics like OR, RR, SMD, and MD. It generates a ready-to-analyze dataset of standardized effect sizes and variances.

Can I calculate effect sizes and variances in R when original studies have missing statistics?▼

Yes, you can calculate effect sizes with missing statistics using R with the metafor and escalc packages. The Skill automates variance estimation and handles incomplete reports to produce analyzable inputs.

What is the best way to prepare a dataset for R metafor pipelines from studies with inconsistent reporting?▼

The best way to prepare data for R metafor pipelines is to input the study dataset into the data-extraction workflow. It converts mixed formats into a standardized dataset complete with computed effect sizes and variances.

Do I need R with escalc and metafor packages to compute effect sizes and variances from raw study data?▼

Yes, you need R with the metafor and escalc packages to compute effect sizes and variances. This Skill requires these specific dependencies to transform raw study data into a common metric for analysis.

Why does meta-analysis data extraction require converting binary and continuous outcomes to a common metric?▼

Meta-analysis data extraction requires converting outcomes to a common metric to synthesize results across mixed reporting formats. This conversion reduces manual calculation errors and prepares a standardized dataset for analysis.

Does this data extraction workflow handle automated variance estimation for incomplete study reports?▼

Yes, the data extraction workflow handles automated variance estimation for incomplete study reports. It specifically addresses missing data to ensure your dataset contains the necessary effect sizes and variances for meta-analysis.

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