git:20260424.c34a0ae to git:20260930.913abeb

40 added, 68 removed. Audit A to A.

---

name: replicate-study

description: Replicate an existing cohort study's methodology on a different database. Extracts study design from a source paper, maps variables to the target DB via harmonization table, generates analysis code, and produces a replication difference report.

triggers: replicate study, replicate paper, 논문 복제, 방법론 복제, reproduce study, replication, 다른 DB로, swap database, 데이터 교체

tools: Read, Write, Edit, Bash, Grep, Glob

model: opus

---

# Replicate Study Skill

- You are assisting a medical researcher in replicating an existing published study's methodology

- on a different database. This is a common research strategy: take a validated methodology from

- Paper A (e.g., NHIS cohort study) and apply it to Database B (e.g., KNHANES, NHANES, or another

- cohort) to produce a new paper with the same analytical rigor.

- ## When to Use

- - Researcher has a published paper they want to replicate on their own data

- - Swapping exposure/outcome variables within the same DB

- - Cross-national replication (e.g., Korean study → US data, or vice versa)

- - Extending a single-institution study to a national cohort

+ 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: `${SKILL_DIR}/references/harmonization_knhanes_nhanes.csv` (if KNHANES↔NHANES)

+ - Default: `${CLAUDE_SKILL_DIR}/references/harmonization_knhanes_nhanes.csv` (if KNHANES↔NHANES)

## Reference Files

- - `${SKILL_DIR}/references/methodology_extraction_template.md` — checklist for extracting study design

- - `${SKILL_DIR}/references/harmonization_knhanes_nhanes.csv` — KNHANES↔NHANES variable mapping (67 rows)

- - `${SKILL_DIR}/references/harmonization_3country.csv` — KNHANES+NHANES+CHNS 3-country mapping (45 rows, if available)

+ - `${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):

- - `medsci-skills/skills/write-paper/references/paper_types/nhis_cohort.md`

- - `medsci-skills/skills/write-paper/references/paper_types/cross_national.md`

- - `medsci-skills/skills/analyze-stats/references/analysis_guides/survey_weighted.md`

- - `medsci-skills/skills/analyze-stats/references/analysis_guides/propensity_score.md`

+ - `${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 methodology using the extraction template:

- - **Study design**: cohort / cross-sectional / case-control

- - **Database**: name, country, years, N

- - **Population**: inclusion/exclusion criteria, age range

- - **Exposure**: variable name, definition, coding

- - **Outcome**: variable name, definition, coding

- - **Covariates**: full list with definitions

- - **Statistical methods**: regression type, adjustment model, subgroup analyses

- - **Survey design**: weights, strata, PSU (if applicable)

- - **Sensitivity analyses**: list all

- 3. Output: structured extraction summary for user review.

+ 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 (CSV with columns: domain, concept, source_var, target_var, notes).

- 2. For each extracted variable (exposure, outcome, covariates):

- - Find the matching row in the harmonization table

- - Flag: DIRECT_MATCH / RECODE_NEEDED / NOT_AVAILABLE / PROXY_AVAILABLE

+ 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 for user approval.

+ 4. Output: variable mapping table (`variable_mapping.csv`) for user approval.

### Phase 3: Code Generation

- 1. Generate analysis code (Python with `pandas` + R via `subprocess` for survey-weighted):

+ 1. Generate self-contained, reproducible analysis code (Python with `pandas` + R via

+ `subprocess` for survey-weighted analysis):

a. **Data loading & cleaning**: read target DB, apply inclusion/exclusion

b. **Variable derivation**: recode variables per mapping table

- c. **Survey design setup**: define svydesign object (strata, PSU, weights)

+ c. **Survey design setup**: define svydesign object (strata, PSU, weights). 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. All code must be self-contained and reproducible.

+ 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

- Generate a structured difference report documenting:

+ 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 calculation) |

+ | Methodological Differences | Any forced changes (e.g., BMI cutoffs, LDL direct measurement vs Friedewald) |

| Expected Differences | Why results may differ (population, measurement, cultural) |

- Save as `replication_report.md` in the working directory.

+ 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

- ## Critical Rules

- 1. **Never pool data across surveys**. Analyze each country's data with its own survey design.

- 2. **Document every deviation** from the source methodology in the difference report.

- 3. **Asian BMI cutoffs** (≥25 for obesity) when analyzing Korean data, even if source used WHO (≥30).

- 4. **LDL calculation**: note if source used direct measurement vs Friedewald.

- 5. **Weighted analysis is mandatory** for KNHANES/NHANES — never run unweighted models.

- 6. **IRB**: note that KNHANES/NHANES are de-identified public data (IRB exempt or waived).

- 7. **Outdated source definitions**: if the source paper 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 current (extension). Document the choice explicitly in the difference report.

## 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)

```

- ## Example Invocation

- ```

- /replicate-study

- Source paper: Joo 2026 (Psychiatry Research) — depression/diabetes cross-national

- Target DB: /path/to/knhanes/HN18.csv

- Harmonization: /path/to/harmonization_knhanes_nhanes.csv

- ```

- ## Anti-Hallucination

- - **Never fabricate variable names, dataset column names, or variable codings.** If a variable mapping is uncertain, output `[VERIFY: variable_name]` and ask the user to confirm against the data dictionary.

- - **Never fabricate statistical results** — no invented p-values, effect sizes, confidence intervals, or sample sizes. All numbers must come from executed code output.

- - **Never generate references from memory.** Use `/search-lit` for all citations.

- - If a function, package, or API does not exist or you are unsure, say so explicitly rather than guessing.