Replicate Study Skill

Apply a published study's methodology to a different database and report every place the target database forced a deviation.

Inputs

1. Source paper: PDF, DOI, or markdown of the paper to replicate
2. Target database path: CSV/SAS data file(s) to use
3. Harmonization table (optional): CSV mapping source → target variables
   • Default: ${CLAUDE_SKILL_DIR}/references/harmonization_knhanes_nhanes.csv (if KNHANES↔NHANES)

Reference Files

• ${CLAUDE_SKILL_DIR}/references/methodology_extraction_template.md — checklist for extracting study design
• ${CLAUDE_SKILL_DIR}/references/harmonization_knhanes_nhanes.csv — KNHANES↔NHANES variable mapping (67 rows)
• ${CLAUDE_SKILL_DIR}/references/harmonization_3country.csv — KNHANES+NHANES+CHNS 3-country mapping (46 rows)
• Upstream templates (read on demand):
  • ${CLAUDE_SKILL_DIR}/../write-paper/references/paper_types/nhis_cohort.md
  • ${CLAUDE_SKILL_DIR}/../write-paper/references/paper_types/cross_national.md
  • ${CLAUDE_SKILL_DIR}/../analyze-stats/references/analysis_guides/survey_weighted.md
  • ${CLAUDE_SKILL_DIR}/../analyze-stats/references/analysis_guides/propensity_score.md

Workflow

Phase 1: Source Paper Analysis

1. Read the source paper (PDF → text, or markdown).
2. Extract the methodology by filling methodology_extraction_template.md: study design, database (name, country, years, N), population (inclusion/exclusion, age range), exposure and outcome (variable, definition, coding), covariates with definitions, statistical methods (regression type, adjustment models, subgroup analyses), survey design (weights, strata, PSU), and every sensitivity analysis.
3. Outdated source definitions: if the source used a pre-2023 definition that has since been superseded (e.g., NAFLD → MASLD 2023, CKD-EPI 2009 → 2021 race-free), call /define-variables to cross-check whether to mirror the legacy definition (pure replication) or upgrade to the current one (extension). Record the choice in the difference report.
4. Output: structured extraction summary for user review.

Phase 2: Variable Mapping

1. Load the harmonization table (columns: domain, concept, concept_en, one variable/label column set per database such as knhanes_var / nhanes_var, and harmonization_notes).
2. For each extracted variable (exposure, outcome, covariates), find the matching row and flag it DIRECT_MATCH / RECODE_NEEDED / NOT_AVAILABLE / PROXY_AVAILABLE. If a mapping is uncertain, write [VERIFY: variable_name] and ask the user to confirm it against the data dictionary; never guess a variable name, column name or coding.
3. Generate a mapping report:
   • Green: directly available (no recoding)
   • Yellow: available but needs recoding (document transformation)
   • Red: not available in target DB (propose proxy or exclusion)
4. Output: variable mapping table (variable_mapping.csv) for user approval.

Phase 3: Code Generation

1. Generate self-contained, reproducible analysis code (Python with pandas + R via subprocess for survey-weighted analysis): a. Data loading & cleaning: read target DB, derive inclusion/exclusion flags (keep every row) b. Variable derivation: recode variables per mapping table c. Survey design setup: define svydesign object (strata, PSU, weights) on the full file, then restrict it with subset(design, <inclusion flag>); deleting rows before the design is declared gives wrong standard errors (/analyze-stats survey_weighted.md, subpopulation analysis). Weighted analysis is mandatory for KNHANES/NHANES — never run unweighted models. Never pool data across surveys; analyze each country's data with its own survey design. d. Table 1: demographics by exposure group (weighted) e. Main analysis: replicate the primary model (logistic/Cox/linear regression) f. Subgroup analyses: if specified in source paper g. Sensitivity analyses: replicate all listed in source paper
2. Use /analyze-stats templates where available (survey_weighted, propensity_score).
3. Use Asian BMI cutoffs (≥25 for obesity) for Korean data, even if the source used WHO (≥30).
4. Run the code on the target data; every number in results/ and the report comes from that executed output.

Phase 4: Difference Report

Save replication_report.md in the working directory, documenting every deviation from the source methodology:

Section	Content
Study Design	Same / Modified (explain)
Database	Source DB → Target DB (N, years, country)
Population	Inclusion/exclusion differences
Variable Mapping	Full mapping table with match status
Unavailable Variables	What's missing and how handled
Methodological Differences	Any forced changes (e.g., BMI cutoffs, LDL direct measurement vs Friedewald)
Expected Differences	Why results may differ (population, measurement, cultural)

Note that KNHANES/NHANES are de-identified public data (IRB exempt or waived). Cite only with a /search-lit-confirmed DOI/PMID; otherwise mark the reference [UNVERIFIED - NEEDS MANUAL CHECK].

Phase 5: Validation Checklist

Before reporting completion, verify:

• All source paper covariates accounted for (mapped, proxied, or documented as missing)
• Survey weights correctly applied (NEVER analyze unweighted if source used weights)
• Obesity/BMI cutoffs match target population standards (Asian vs WHO)
• Fasting requirements matched (fasting glucose, lipids)
• Age restrictions applied correctly
• Code runs without errors on target data
• Output tables match source paper structure

Output Files

{working_dir}/
├── replication_report.md     — Structured difference report
├── variable_mapping.csv      — Variable mapping table with match status
├── analysis_code.py          — Main analysis script (Python + R calls)
├── analysis_code.R           — R script for survey-weighted analysis
└── results/
    ├── table1.csv            — Demographics table
    ├── main_results.csv      — Primary analysis results
    └── subgroup_results.csv  — Subgroup analysis results (if applicable)