Cross-National Comparison Study Skill

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's references/harmonization_knhanes_nhanes.csv

Reference Files

• /write-paper's references/paper_types/cross_national.md — writing template
• /analyze-stats's references/analysis_guides/survey_weighted.md — survey-weighted analysis guide
• references/chns_coding.md — CHNS files, merge keys, coding and warnings; read it when the design

includes China (3-country design)

• references/additional_variables.md — asthma, sleep, physical activity, diet, treatment and

non-HDL-cholesterol coding, plus composite-score (LE8) warnings; read it when the study uses any of them

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

1. Check harmonization table for variable availability
2. Output: study protocol summary for user approval

Phase 2: Data Preparation

Never guess a variable name, dataset column name, or variable coding. If a mapping is uncertain, output [VERIFY: variable_name] and ask the user to confirm it against the data dictionary.

KNHANES (single CSV):

1. Load CSV and keep every row — the age ≥20 (or per-protocol) restriction is applied to the design in step 3
2. Derive variables using KNHANES coding:

1. Set survey design on the full file, then restrict to the analytic domain:

des <- svydesign(id=~psu, strata=~kstrata, weights=~wt_itvex, nest=TRUE, data=df); des_ad <- subset(des, age >= 20). Never filter rows before svydesign() — dropping them changes the standard errors (see survey_weighted.md, subpopulation analysis)

NHANES (multiple CSVs):

1. Load and merge tables by SEQN (DEMO_J, DPQ_J, GHB_J, GLU_J, BMX_J, SMQ_J, ALQ_J, DIQ_J, MCQ_J, BPQ_J, BPXO_J)
2. Derive variables using NHANES coding. CRITICAL: NHANES data downloaded via R nhanesA package

uses TEXT LABELS, not numeric codes.

1. Set survey design on the full file, then subset() the design object to the analytic domain

(e.g. RIDAGEYR >= 20): svydesign(id=~SDMVPSU, strata=~SDMVSTRA, weights=~WTMEC2YR, nest=TRUE). The weight follows the files: the single-cycle _J tables above take WTMEC2YR; WTMECPRP goes only with the pre-pandemic P_ files (P_DEMO, P_BMX, ...). A variable from the fasting subsample (GLU_J LBXGLU, TRIGLY_J LBXTR/LBDLDL) takes the fasting weight instead: WTSAF2YR (_J) or WTSAFPRP (P_); a variable that combines a fasting-subsample component with full-sample ones (diabetes above) is defined only in that fasting domain, so it cannot enter a WTMEC2YR model. Pooling cycles follows the NCHS rules: divide each cycle's weight by the number of cycles pooled (1999–2002 has its own 4-year weights), and to combine 2015–2016 with 2017–March 2020 use 2/5.2 × WTMEC2YR and 3.2/5.2 × WTMECPRP.

CHNS (3-country design): read references/chns_coding.md before preparing China data.

Phase 3: Parallel Analysis

For EACH country independently:

1. Table 1: Baseline characteristics by exposure (weighted counts + percentages)
2. Main analysis: Sequential logistic regression models

• Model 1 (unadjusted)
• Model 2 (age + sex)
• Model 3 (fully adjusted: + education, income, smoking, alcohol, obesity, CVD)

1. Subgroup analyses: By sex, age group, education, income, alcohol, smoking, CVD, obesity.

Whether the association differs between subgroups is tested with an exposure × subgroup interaction term fitted on the full design (svyglm on the whole sample), not by comparing the subgroups' P values.

1. Dose-response (if applicable): RCS with 3 knots

Phase 4: Cross-National Comparison Table

Generate a side-by-side comparison:

Compare the countries with the ratio of their odds ratios, not with whether the directions agree: two estimates in the same direction can differ, and opposite directions can be compatible. With log odds ratios b₁, b₂ and their design-based standard errors SE₁, SE₂ from the two independent surveys, the ratio is exp(b₁ − b₂) with 95% CI exp(b₁ − b₂ ± 1.96·√(SE₁² + SE₂²)) and z = (b₁ − b₂)/√(SE₁² + SE₂²) (Altman & Bland, BMJ 2003;326:219).

Every number comes from executed code output (analysis_korea.R, analysis_us.R) — never an invented p-value, effect size, confidence interval, or sample size.

Phase 5: Output Files

{working_dir}/
├── cross_national_report.md    — Study summary + comparison tables
├── variable_mapping.csv        — Variable mapping with match status
├── analysis_korea.R            — KNHANES analysis (self-contained)
├── analysis_us.R               — NHANES analysis (self-contained)
├── results/
│   ├── table1_korea.csv
│   ├── table1_us.csv
│   ├── main_results_comparison.csv
│   └── subgroup_comparison.csv
└── manuscript_draft/           — Optional: Methods + Results draft
    ├── methods_draft.md
    └── results_draft.md

Take every citation in the report or draft from /search-lit (confirmed DOI/PMID); mark any other [UNVERIFIED - NEEDS MANUAL CHECK], and never generate references from memory.

Critical Rules

1. NEVER pool data across countries. Each country analyzed with its own survey design.
2. Country-specific BMI cutoffs: Korea ≥25 (Asian), US ≥30 (WHO).
3. Country-specific income: KNHANES quartile, NHANES PIR → harmonize to binary.
4. Weighted analysis mandatory: Both KNHANES and NHANES are complex surveys. CHNS has no

survey weights — analyse it unweighted (see references/chns_coding.md).

1. Document all harmonization decisions: What matches, what needed recoding, what differs.
2. Same analytic approach: Identical model specifications for both countries for fair comparison.