Clinical Data Cleaner

Clean, validate, and standardize clinical trial data to meet CDISC SDTM standards for regulatory submissions to FDA or EMA.

Quick Start

from scripts.main import ClinicalDataCleaner

# Initialize for Demographics domain
cleaner = ClinicalDataCleaner(domain='DM')

# Clean data with default settings
cleaned = cleaner.clean(raw_data)

# Save with audit trail
cleaner.save_report('output.csv')

Core Capabilities

1. SDTM Domain Validation

cleaner = ClinicalDataCleaner(domain='DM')  # or 'LB', 'VS'
is_valid, missing = cleaner.validate_domain(data)

Required Fields:

• DM: STUDYID, USUBJID, SUBJID, RFSTDTC, RFENDTC, SITEID, AGE, SEX, RACE
• LB: STUDYID, USUBJID, LBTESTCD, LBCAT, LBORRES, LBORRESU, LBSTRESC, LBDTC
• VS: STUDYID, USUBJID, VSTESTCD, VSORRES, VSORRESU, VSSTRESC, VSDTC

2. Missing Value Handling

cleaner = ClinicalDataCleaner(
    domain='DM',
    missing_strategy='median'  # mean, median, mode, forward, drop
)
cleaned = cleaner.handle_missing_values(data)

3. Outlier Detection

cleaner = ClinicalDataCleaner(
    domain='LB',
    outlier_method='domain',  # iqr, zscore, domain
    outlier_action='flag'     # flag, remove, cap
)
flagged = cleaner.detect_outliers(data)

Clinical Thresholds:

4. Date Standardization

standardized = cleaner.standardize_dates(data)
# Converts to ISO 8601: 2023-01-15T09:30:00

5. Complete Pipeline

cleaner = ClinicalDataCleaner(
    domain='DM',
    missing_strategy='median',
    outlier_method='iqr',
    outlier_action='flag'
)
cleaned_data = cleaner.clean(data)
cleaner.save_report('output.csv')

Output Files:

• output.csv - Cleaned SDTM data
• output.report.json - Audit trail for regulatory submission

CLI Usage

# Clean demographics
python scripts/main.py \
  --input dm_raw.csv \
  --domain DM \
  --output dm_clean.csv \
  --missing-strategy median \
  --outlier-method iqr \
  --outlier-action flag

# Clean lab data with clinical thresholds
python scripts/main.py \
  --input lb_raw.csv \
  --domain LB \
  --output lb_clean.csv \
  --outlier-method domain

Common Patterns

See references/common-patterns.md for detailed examples:

• Regulatory Submission Preparation
• Interim Analysis Data Preparation
• Database Migration Cleanup
• External Lab Data Integration

Troubleshooting

See references/troubleshooting.md for solutions to:

• Validation failures
• Date parsing errors
• Memory errors with large datasets
• Outlier detection issues

Quality Checklist

Pre-Cleaning:

• IACUC approval obtained (animal studies)
• Sample size adequately powered
• Randomization method documented

Post-Cleaning:

• Validate against CDISC SDTM IG
• Review all cleaning actions in audit trail
• Test import to analysis software

References

• references/sdtm_ig_guide.md - CDISC SDTM Implementation Guide
• references/domain_specs.json - Domain-specific field requirements
• references/outlier_thresholds.json - Clinical outlier thresholds
• references/common-patterns.md - Detailed usage patterns
• references/troubleshooting.md - Problem-solving guide

Skill ID: 189 | Version: 2.0 | License: MIT