edc-data-validation

name	edc-data-validation
description	Generate code for EDC export validation, clinical data range checks, cross-form consistency checks, SDTM structure validation, controlled terminology verification, and define.xml generation. Use when the user asks to validate clinical trial data exports, check vital sign or lab value ranges, verify AE date consistency, validate SDTM datasets against CDISC rules, generate define.xml, or build an automated data review pipeline. Triggers include "EDC validation", "range check clinical data", "cross-form consistency", "SDTM validation", "controlled terminology check", "define.xml generation", "Medidata Rave export", "Oracle InForm", "Veeva Vault CDMS", "clinical data cleaning", "edit check", "data query", "SDTM structure check", "lab range check", "vital signs validation", "AE date check", "protocol deviation detection", "OpenCDISC", "Pinnacles 21", "P21 validation".
usage	Invoke to generate Python or R code for clinical data validation and SDTM compliance checks.
version	1.0.0
validated_against	{"date":"2025-01-15T00:00:00.000Z","packages":{"pinnacle21":"4.0","define-xml":"2.1"}}
tags	["skill","category:pipeline","clinical-data","edc","sdtm","cdisc","validation","define-xml","hcls"]

EDC Data Validation — Pipeline Skill

Overview

You generate deterministic Python code for clinical data validation pipelines. When the user provides EDC exports or SDTM datasets, produce validation code following the recipes below. Always confirm the input format and therapeutic area before generating.

Usage

• Validate EDC exports with range checks, cross-form consistency, and partial date imputation
• Check SDTM datasets against CDISC controlled terminology and structure rules
• Generate define.xml and prioritized validation reports

Core Concepts

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Response Format

• Lead with the command or code the user needs — explain after
• Structure as: confirm inputs → working code → key parameters explained → gotchas
• One complete working example per task; do not show every alternative
• Keep code comments minimal and functional (what, not why-it-exists)
• Target: 50-100 lines of code with brief surrounding explanation

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1. Validation Priority Decision Tree

Use this to triage findings and assign query priority:

Issue Detected
├── Missing/invalid RFSTDTC or DSSTDTC? → CRITICAL (blocks submission)
├── AE date outside study period (before consent or after completion)? → CRITICAL
├── CT violation on required codelist (SEX, AEOUT, AESER)? → CRITICAL
├── Out-of-range vital sign WITHOUT data comment? → HIGH
├── Missing AESER when AEOUT='FATAL' or SAE reported? → HIGH
├── Lab value triggers Hy's Law flag (ALT/AST >3×ULN + BILI >2×ULN)? → HIGH
├── Minor CT mismatch (case difference, trailing space)? → MEDIUM
├── Missing optional variable (AEENDTC, AESEV)? → MEDIUM
├── Formatting issue (date separators, decimal precision)? → LOW
└── Case sensitivity only (e.g., "Male" vs "M")? → LOW

Severity → Action mapping:

• CRITICAL → Auto-query, blocks dataset lock
• HIGH → Query within 48h, reviewer will flag
• MEDIUM → Batch query at next data cut
• LOW → Note for final clean-up, no query needed

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2. Therapeutic-Area Decision Tree

Select range tables based on study type:

What is the therapeutic area?
├── Oncology
│   ├── Use RECIST response values: CR, PR, SD, PD, NE
│   ├── Tumor measurement: 0–300 mm (flag >200 as HIGH)
│   └── ECOG valid values: 0, 1, 2, 3, 4, 5
├── Cardiology
│   ├── QTcF: normal <450ms, borderline 450–480ms, prolonged >480ms, critical >500ms
│   ├── SYSBP: 70–200 mmHg (tighter than general)
│   └── HR: 40–150 beats/min (tighter than general)
├── Pediatric (age <18)
│   ├── SYSBP: multiply adult low by 0.7, high by 0.8
│   ├── HR: multiply adult high by 1.3 (higher resting HR)
│   ├── WEIGHT: 2–120 kg
│   └── HEIGHT: 30–200 cm
└── General / Geriatric (age ≥65)
    ├── Use standard ranges (§3 below)
    └── Flag HR >180 as CRITICAL (not just HIGH)