cross-national

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End-to-end cross-national comparison study using KNHANES + NHANES + CHNS (or other parallel surveys). Variable harmonization, parallel weighted analysis, and comparison tables. Supports 2-country (KR+US) and 3-country (KR+US+CN) designs.

AI & Automation 223 stars 55 forks Updated yesterday MIT

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Skill Content

# Cross-National Comparison Study Skill You are assisting a medical researcher in conducting a cross-national comparison study using parallel nationally representative surveys (e.g., KNHANES for Korea, NHANES for the US, CHNS for China). ## When to Use - Researcher has a clinical question to compare across two countries - KNHANES + NHANES data available (or other parallel survey pairs) - Goal: produce a complete analysis with country-stratified results + comparison table ## Inputs 1. **Research question**: exposure → outcome association to compare across countries 2. **Korean data path**: KNHANES CSV file 3. **US data path**: NHANES CSV directory (multiple tables to merge) 4. **Harmonization table** (optional): CSV mapping variables across surveys - Default: replicate-study skill's `harmonization_knhanes_nhanes.csv` ## Reference Files - Harmonization table: `medsci-skills/skills/replicate-study/references/harmonization_knhanes_nhanes.csv` - Upstream: - `medsci-skills/skills/write-paper/references/paper_types/cross_national.md` — writing template - `medsci-skills/skills/analyze-stats/references/analysis_guides/survey_weighted.md` ## Workflow ### Phase 1: Study Definition 1. Confirm research question: Exposure → Outcome 2. Define variable coding for both countries: - Exposure: PHQ-9, BMI category, smoking, etc. - Outcome: diabetes, hypertension, mortality, etc. - Covariates: age, sex, education, income, smoking, alcohol, obesity, CVD 3. Check harmoniza...

Details

Author
Aperivue
Repository
Aperivue/medsci-skills
Created
3 months ago
Last Updated
yesterday
Language
Python
License
MIT

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