Replicate Study

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

Inputs

• Source paper: PDF, DOI, or markdown of the paper to replicate
• Target database path: CSV/SAS data file(s) to use
• Harmonization table (optional): CSV mapping source → target variables
• Default: ${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)
• 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

Workflow

Phase 1: Source Paper Analysis

• Read the source paper (PDF → text, or markdown).
• 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
• Output: structured extraction summary for user review.

Phase 2: Variable Mapping

• Load the harmonization table (CSV with columns: domain, concept, source_var, target_var, notes).
• For each extracted variable (exposure, outcome, covariates):
• Find the matching row in the harmonization table
• Flag: DIRECT_MATCH / RECODE_NEEDED / NOT_AVAILABLE / PROXY_AVAILABLE
• 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)
• Output: variable mapping table for user approval.

Phase 3: Code Generation

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

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)

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

• Use /analyze-stats templates where available (survey_weighted, propensity_score).
• All code must be self-contained and reproducible.

Phase 4: Difference Report

Generate a structured difference report documenting:

| 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) |

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

Save as replication_report.md in the working directory.

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

• Never pool data across surveys. Analyze each country's data with its own survey design.
• Document every deviation from the source methodology in the difference report.
• Asian BMI cutoffs (≥25 for obesity) when analyzing Korean data, even if source used WHO (≥30).
• LDL calculation: note if source used direct measurement vs Friedewald.
• Weighted analysis is mandatory for KNHANES/NHANES — never run unweighted models.
• IRB: note that KNHANES/NHANES are de-identified public data (IRB exempt or waived).
• 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.