/replicate-study

name: replicate-study
description: Use when applying a published cohort study's methodology to a different database. Extracts the design from the source paper, maps variables to the target database with a harmonization table, generates the analysis code and reports every forced deviation.
model: opus
metadata:
  triggers: "replicate study, replicate paper, 논문 복제, 방법론 복제, reproduce study, replication, 다른 DB로, swap database, 데이터 교체"

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 **Outdated source definitions**: if the source used a pre-2023 definition that has since been superseded (e.g., NAFLD → MASLD 2023, CKD-EPI 2009 → 2021 race-free), call `/define-variables` to cross-check whether to mirror the legacy definition (pure replication)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