analyze-stats

Statistical Analysis Skill

Generate reproducible code (Python preferred, R when necessary) for medical research analyses, run it, and produce publication-ready tables and figures from its output.

Data Privacy Check

Before reading any data file, check whether it might contain Protected Health Information (PHI):

1. If *_deidentified.* files exist in the working directory, use those.
2. If only raw CSV/Excel files exist, ask the user whether the data contain patient identifiers (names, national ID / RRN, contact details); if so, have them de-identify it first with /deidentify.
3. Proceed once the user confirms the data are de-identified or contain no PHI.
4. NEVER display raw PHI values (names, phone numbers, RRN) in your output, because console output is copied into transcripts and drafts. If you encounter them, warn the user and suggest /deidentify.

Reference Files

• ${CLAUDE_SKILL_DIR}/references/templates/ — reusable analysis scripts; read the matching one before generating code.
• ${CLAUDE_SKILL_DIR}/references/analysis_guides/ — methodology guides; the Phase 2 table says which to read.
• ${CLAUDE_SKILL_DIR}/references/table-standards/ — table-standards.md (universal and AMA rules, footnote order, gtsummary pipeline), journal-profiles/ (YAML per journal: radiology, jama, nejm, lancet, european_radiology, ajr), table-types/ (one template per table type), tool-comparison.md (R/Python table tools).
• ${CLAUDE_SKILL_DIR}/references/style/figure_style.mplstyle — figure style.

Workflow

Phase 1: Data Assessment

1. Read the data file (CSV, Excel, TSV, or other tabular format).
2. Report to the user: shape (rows x columns); column names and inferred types (continuous, categorical, ordinal, binary, datetime); missing values per column (count and percentage); first 5 rows; unique value counts for categorical columns.
3. Identify the analysis unit: patient, exam, lesion, image, rater, study, etc.
4. If a variable name or coding is uncertain, write [VERIFY: variable_name] and ask the user to confirm it against the data dictionary; never guess a column name or coding.